-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroa
> d/LAPOP 2016 original datasets/bateson_weintraub_trumpeffect.smcl
  log type:  smcl
 opened on:   8 Aug 2021, 19:06:19

. do "/var/folders/3f/yt_wp9cn08vgf79zpwdbf4fc0000gn/T//SD24318.000000"

. *** Code for The 2016 Election & America's Standing Abroad
. *** Regina Bateson and Michael Weintraub
. *** Journal of Politics
. *** This final version is 8/8/2021 
. 
. //Table of Contents//
. 
. //1. Prepare the data
. //2. Balance Tests for Figure 1
. //3. Main results for Figure 2
. //4. Changes in predicted probability of trusting US government, by country f
> or Figure 3
. //5. Placebo tests for Figures 4 and 5
. //6. Tables, results & figures for Appendix A
. //7. Tables, results & figures for Appendix B
. 
. *****************************************
. *****************************************
. //1. PREPARE THE DATA//
. *****************************************
. *****************************************
. 
. //Setup: install packages and set scheme for figures
. ssc install blindschemes, replace all
checking blindschemes consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install addplot
checking addplot consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install texdoc
checking texdoc consistency and verifying not already installed...
all files already exist and are up to date.

. ssc install ebalance
checking ebalance consistency and verifying not already installed...
all files already exist and are up to date.

. set scheme plottigblind

. 
. 
. //Download data//
. 
. //We use the original LAPOP 2016 country files. Due to restrictions imposed b
> y LAPOP, 
. //we are unable to post the original LAPOP data files, so users must download
>  them. The datasets are free &
. //available from http://datasets.americasbarometer.org/database/login.php
. 
. //Users should save the datasets using the following names: "paraguay.dta" "s
> alvador.dta" "dr.dta" "honduras.dta"
. 
. //Merge datasets
. 
. use "paraguay.dta", clear
(�AmericasBarometer, LAPOP; created 19 Sep 2017; type: notes list)

. append using "salvador.dta"
(label sex_esp already defined)
(label sex_eng already defined)
(label uniq_id_esp already defined)
(label uniq_id_eng already defined)
(label idiomaq_esp already defined)
(label idiomaq_eng already defined)
(label env2b_esp already defined)
(label env2b_eng already defined)
(label env1c_esp already defined)
(label env1c_eng already defined)
(label drk1_esp already defined)
(label drk1_eng already defined)
(label dst1b_esp already defined)
(label dst1b_eng already defined)
(label mil10un_esp already defined)
(label mil10un_eng already defined)
(label mil10oas_esp already defined)
(label mil10oas_eng already defined)
(label mil10e_esp already defined)
(label mil10e_eng already defined)
(label mil10a_esp already defined)
(label mil10a_eng already defined)
(label nationality_esp already defined)
(label nationality_eng already defined)
(label sexi_esp already defined)
(label sexi_eng already defined)
(label iarea7_esp already defined)
(label iarea7_eng already defined)
(label iarea6_esp already defined)
(label iarea6_eng already defined)
(label iarea4_esp already defined)
(label iarea4_eng already defined)
(label iarea3_esp already defined)
(label iarea3_eng already defined)
(label iarea2_esp already defined)
(label iarea2_eng already defined)
(label iarea1_esp already defined)
(label iarea1_eng already defined)
(label conocim_esp already defined)
(label conocim_eng already defined)
(label colorr_esp already defined)
(label colorr_eng already defined)
(label formatq_esp already defined)
(label formatq_eng already defined)
(label r16_esp already defined)
(label r16_eng already defined)
(label r1_esp already defined)
(label r1_eng already defined)
(label r18_esp already defined)
(label r18_eng already defined)
(label r15_esp already defined)
(label r15_eng already defined)
(label r14_esp already defined)
(label r14_eng already defined)
(label r12_esp already defined)
(label r12_eng already defined)
(label r8_esp already defined)
(label r8_eng already defined)
(label r7_esp already defined)
(label r7_eng already defined)
(label r6_esp already defined)
(label r6_eng already defined)
(label r5_esp already defined)
(label r5_eng already defined)
(label r4a_esp already defined)
(label r4a_eng already defined)
(label r4_esp already defined)
(label r4_eng already defined)
(label r3_esp already defined)
(label r3_eng already defined)
(label pr1_esp already defined)
(label pr1_eng already defined)
(label gi0_esp already defined)
(label gi0_eng already defined)
(label www1_esp already defined)
(label www1_eng already defined)
(label etid_esp already defined)
(label etid_eng already defined)
(label q12f_esp already defined)
(label q12f_eng already defined)
(label q12m_esp already defined)
(label q12m_eng already defined)
(label q12_esp already defined)
(label q12_eng already defined)
(label q12bn_esp already defined)
(label q12bn_eng already defined)
(label q12c_esp already defined)
(label q12c_eng already defined)
(label q11n_esp already defined)
(label q11n_eng already defined)
(label q10e_esp already defined)
(label q10e_eng already defined)
(label q10d_esp already defined)
(label q10d_eng already defined)
(label q14_esp already defined)
(label q14_eng already defined)
(label q10a_esp already defined)
(label q10a_eng already defined)
(label q10new_esp already defined)
(label q10new_eng already defined)
(label q10g_esp already defined)
(label q10g_eng already defined)
(label ocup1a_esp already defined)
(label ocup1a_eng already defined)
(label ocup4a_esp already defined)
(label ocup4a_eng already defined)
(label q3c_esp already defined)
(label q3c_eng already defined)
(label q5b_esp already defined)
(label q5b_eng already defined)
(label q5a_esp already defined)
(label q5a_eng already defined)
(label ed2_esp already defined)
(label ed2_eng already defined)
(label ed_esp already defined)
(label ed_eng already defined)
(label cct1b_esp already defined)
(label cct1b_eng already defined)
(label wf1_esp already defined)
(label wf1_eng already defined)
(label exp_b_esp already defined)
(label exp_b_eng already defined)
(label for5_esp already defined)
(label for5_eng already defined)
(label clien1na_esp already defined)
(label clien1na_eng already defined)
(label vb20_esp already defined)
(label vb20_eng already defined)
(label pol1_esp already defined)
(label pol1_eng already defined)
(label vb11_esp already defined)
(label vb11_eng already defined)
(label vb10_esp already defined)
(label vb10_eng already defined)
(label vb3n_esp already defined)
(label vb3n_eng already defined)
(label vb2_esp already defined)
(label vb2_eng already defined)
(label vb1_esp already defined)
(label vb1_eng already defined)
(label exc7_esp already defined)
(label exc7_eng already defined)
(label exc7new_esp already defined)
(label exc7new_eng already defined)
(label exc18_esp already defined)
(label exc18_eng already defined)
(label exc16_esp already defined)
(label exc16_eng already defined)
(label exc15_esp already defined)
(label exc15_eng already defined)
(label exc14_esp already defined)
(label exc14_eng already defined)
(label exc13_esp already defined)
(label exc13_eng already defined)
(label exc11_esp already defined)
(label exc11_eng already defined)
(label exc20_esp already defined)
(label exc20_eng already defined)
(label exc6_esp already defined)
(label exc6_eng already defined)
(label exc2_esp already defined)
(label exc2_eng already defined)
(label lib4_esp already defined)
(label lib4_eng already defined)
(label lib2c_esp already defined)
(label lib2c_eng already defined)
(label lib2b_esp already defined)
(label lib2b_eng already defined)
(label lib1_esp already defined)
(label lib1_eng already defined)
(label d6_esp already defined)
(label d6_eng already defined)
(label d5_esp already defined)
(label d5_eng already defined)
(label d4_esp already defined)
(label d4_eng already defined)
(label d3_esp already defined)
(label d3_eng already defined)
(label d2_esp already defined)
(label d2_eng already defined)
(label d1_esp already defined)
(label d1_eng already defined)
(label e5_esp already defined)
(label e5_eng already defined)
(label w14a_esp already defined)
(label w14a_eng already defined)
(label pn4_esp already defined)
(label pn4_eng already defined)
(label exp_a_esp already defined)
(label exp_a_eng already defined)
(label media4_esp already defined)
(label media4_eng already defined)
(label media3_esp already defined)
(label media3_eng already defined)
(label aoj22new_esp already defined)
(label aoj22new_eng already defined)
(label eff2_esp already defined)
(label eff2_eng already defined)
(label eff1_esp already defined)
(label eff1_eng already defined)
(label ing4_esp already defined)
(label ing4_eng already defined)
(label ros4_esp already defined)
(label ros4_eng already defined)
(label ros1_esp already defined)
(label ros1_eng already defined)
(label infra3_esp already defined)
(label infra3_eng already defined)
(label infrax_esp already defined)
(label infrax_eng already defined)
(label sd6new2_esp already defined)
(label sd6new2_eng already defined)
(label sd3new2_esp already defined)
(label sd3new2_eng already defined)
(label sd2new2_esp already defined)
(label sd2new2_eng already defined)
(label m2_esp already defined)
(label m2_eng already defined)
(label m1_esp already defined)
(label m1_eng already defined)
(label b47a_esp already defined)
(label b47a_eng already defined)
(label b37_esp already defined)
(label b37_eng already defined)
(label b32_esp already defined)
(label b32_eng already defined)
(label b21a_esp already defined)
(label b21a_eng already defined)
(label b21_esp already defined)
(label b21_eng already defined)
(label b18_esp already defined)
(label b18_eng already defined)
(label b13_esp already defined)
(label b13_eng already defined)
(label b12_esp already defined)
(label b12_eng already defined)
(label b43_esp already defined)
(label b43_eng already defined)
(label b6_esp already defined)
(label b6_eng already defined)
(label b4_esp already defined)
(label b4_eng already defined)
(label b3_esp already defined)
(label b3_eng already defined)
(label b2_esp already defined)
(label b2_eng already defined)
(label b1_esp already defined)
(label b1_eng already defined)
(label aoj12_esp already defined)
(label aoj12_eng already defined)
(label aoj11_esp already defined)
(label aoj11_eng already defined)
(label vic1exta_esp already defined)
(label vic1exta_eng already defined)
(label vic1ext_esp already defined)
(label vic1ext_eng already defined)
(label jc15a_esp already defined)
(label jc15a_eng already defined)
(label jc13_esp already defined)
(label jc13_eng already defined)
(label jc10_esp already defined)
(label jc10_eng already defined)
(label prot3_esp already defined)
(label prot3_eng already defined)
(label l1_esp already defined)
(label l1_eng already defined)
(label it1_esp already defined)
(label it1_eng already defined)
(label cp20_esp already defined)
(label cp20_eng already defined)
(label cp13_esp already defined)
(label cp13_eng already defined)
(label cp8_esp already defined)
(label cp8_eng already defined)
(label cp7_esp already defined)
(label cp7_eng already defined)
(label cp6_esp already defined)
(label cp6_eng already defined)
(label np1_esp already defined)
(label np1_eng already defined)
(label idio2_esp already defined)
(label idio2_eng already defined)
(label soct2_esp already defined)
(label soct2_eng already defined)
(label a4_esp already defined)
(label a4_eng already defined)
(label ls3_esp already defined)
(label ls3_eng already defined)
(label q1_esp already defined)
(label q1_eng already defined)
(label municipio_esp already defined)
(label municipio_eng already defined)
(label prov_esp already defined)
(label prov_eng already defined)
(label ur_esp already defined)
(label ur_eng already defined)
(label estratosec_esp already defined)
(label estratosec_eng already defined)
(label tamano_esp already defined)
(label tamano_eng already defined)
(label estratopri_esp already defined)
(label estratopri_eng already defined)
(label pais_esp already defined)
(label pais_eng already defined)

. append using "dr.dta"
(note: variable q2 was byte, now int to accommodate using data's values)
(label sex_esp already defined)
(label sex_eng already defined)
(label uniq_id_esp already defined)
(label uniq_id_eng already defined)
(label idiomaq_esp already defined)
(label idiomaq_eng already defined)
(label env2b_esp already defined)
(label env2b_eng already defined)
(label env1c_esp already defined)
(label env1c_eng already defined)
(label drk1_esp already defined)
(label drk1_eng already defined)
(label dst1b_esp already defined)
(label dst1b_eng already defined)
(label mil10un_esp already defined)
(label mil10un_eng already defined)
(label mil10oas_esp already defined)
(label mil10oas_eng already defined)
(label mil10e_esp already defined)
(label mil10e_eng already defined)
(label mil10a_esp already defined)
(label mil10a_eng already defined)
(label nationality_esp already defined)
(label nationality_eng already defined)
(label sexi_esp already defined)
(label sexi_eng already defined)
(label iarea7_esp already defined)
(label iarea7_eng already defined)
(label iarea6_esp already defined)
(label iarea6_eng already defined)
(label iarea4_esp already defined)
(label iarea4_eng already defined)
(label iarea3_esp already defined)
(label iarea3_eng already defined)
(label iarea2_esp already defined)
(label iarea2_eng already defined)
(label iarea1_esp already defined)
(label iarea1_eng already defined)
(label conocim_esp already defined)
(label conocim_eng already defined)
(label colorr_esp already defined)
(label colorr_eng already defined)
(label formatq_esp already defined)
(label formatq_eng already defined)
(label r16_esp already defined)
(label r16_eng already defined)
(label r1_esp already defined)
(label r1_eng already defined)
(label r18_esp already defined)
(label r18_eng already defined)
(label r15_esp already defined)
(label r15_eng already defined)
(label r14_esp already defined)
(label r14_eng already defined)
(label r12_esp already defined)
(label r12_eng already defined)
(label r8_esp already defined)
(label r8_eng already defined)
(label r7_esp already defined)
(label r7_eng already defined)
(label r6_esp already defined)
(label r6_eng already defined)
(label r5_esp already defined)
(label r5_eng already defined)
(label r4a_esp already defined)
(label r4a_eng already defined)
(label r4_esp already defined)
(label r4_eng already defined)
(label r3_esp already defined)
(label r3_eng already defined)
(label pr1_esp already defined)
(label pr1_eng already defined)
(label gi0_esp already defined)
(label gi0_eng already defined)
(label www1_esp already defined)
(label www1_eng already defined)
(label etid_esp already defined)
(label etid_eng already defined)
(label q12f_esp already defined)
(label q12f_eng already defined)
(label q12m_esp already defined)
(label q12m_eng already defined)
(label q12_esp already defined)
(label q12_eng already defined)
(label q12bn_esp already defined)
(label q12bn_eng already defined)
(label q12c_esp already defined)
(label q12c_eng already defined)
(label q11n_esp already defined)
(label q11n_eng already defined)
(label q10e_esp already defined)
(label q10e_eng already defined)
(label q10d_esp already defined)
(label q10d_eng already defined)
(label q14_esp already defined)
(label q14_eng already defined)
(label q10a_esp already defined)
(label q10a_eng already defined)
(label q10new_esp already defined)
(label q10new_eng already defined)
(label q10g_esp already defined)
(label q10g_eng already defined)
(label ocup1a_esp already defined)
(label ocup1a_eng already defined)
(label ocup4a_esp already defined)
(label ocup4a_eng already defined)
(label q3c_esp already defined)
(label q3c_eng already defined)
(label q5b_esp already defined)
(label q5b_eng already defined)
(label q5a_esp already defined)
(label q5a_eng already defined)
(label ed2_esp already defined)
(label ed2_eng already defined)
(label ed_esp already defined)
(label ed_eng already defined)
(label cct1b_esp already defined)
(label cct1b_eng already defined)
(label wf1_esp already defined)
(label wf1_eng already defined)
(label exp_b_esp already defined)
(label exp_b_eng already defined)
(label for5_esp already defined)
(label for5_eng already defined)
(label vb20_esp already defined)
(label vb20_eng already defined)
(label pol1_esp already defined)
(label pol1_eng already defined)
(label vb11_esp already defined)
(label vb11_eng already defined)
(label vb10_esp already defined)
(label vb10_eng already defined)
(label vb3n_esp already defined)
(label vb3n_eng already defined)
(label vb2_esp already defined)
(label vb2_eng already defined)
(label inf1_esp already defined)
(label inf1_eng already defined)
(label vb1_esp already defined)
(label vb1_eng already defined)
(label exc7new_esp already defined)
(label exc7new_eng already defined)
(label exc18_esp already defined)
(label exc18_eng already defined)
(label exc16_esp already defined)
(label exc16_eng already defined)
(label exc15_esp already defined)
(label exc15_eng already defined)
(label exc14_esp already defined)
(label exc14_eng already defined)
(label exc13_esp already defined)
(label exc13_eng already defined)
(label exc11_esp already defined)
(label exc11_eng already defined)
(label exc20_esp already defined)
(label exc20_eng already defined)
(label exc6_esp already defined)
(label exc6_eng already defined)
(label exc2_esp already defined)
(label exc2_eng already defined)
(label lib4_esp already defined)
(label lib4_eng already defined)
(label lib2c_esp already defined)
(label lib2c_eng already defined)
(label lib2b_esp already defined)
(label lib2b_eng already defined)
(label lib1_esp already defined)
(label lib1_eng already defined)
(label d6_esp already defined)
(label d6_eng already defined)
(label d5_esp already defined)
(label d5_eng already defined)
(label d4_esp already defined)
(label d4_eng already defined)
(label d3_esp already defined)
(label d3_eng already defined)
(label d2_esp already defined)
(label d2_eng already defined)
(label d1_esp already defined)
(label d1_eng already defined)
(label e5_esp already defined)
(label e5_eng already defined)
(label w14a_esp already defined)
(label w14a_eng already defined)
(label pn4_esp already defined)
(label pn4_eng already defined)
(label exp_a_esp already defined)
(label exp_a_eng already defined)
(label media4_esp already defined)
(label media4_eng already defined)
(label media3_esp already defined)
(label media3_eng already defined)
(label aoj22new_esp already defined)
(label aoj22new_eng already defined)
(label eff2_esp already defined)
(label eff2_eng already defined)
(label eff1_esp already defined)
(label eff1_eng already defined)
(label ing4_esp already defined)
(label ing4_eng already defined)
(label ros4_esp already defined)
(label ros4_eng already defined)
(label ros1_esp already defined)
(label ros1_eng already defined)
(label infra3_esp already defined)
(label infra3_eng already defined)
(label infrax_esp already defined)
(label infrax_eng already defined)
(label sd6new2_esp already defined)
(label sd6new2_eng already defined)
(label sd3new2_esp already defined)
(label sd3new2_eng already defined)
(label sd2new2_esp already defined)
(label sd2new2_eng already defined)
(label m2_esp already defined)
(label m2_eng already defined)
(label m1_esp already defined)
(label m1_eng already defined)
(label pr3e_esp already defined)
(label pr3e_eng already defined)
(label pr3d_esp already defined)
(label pr3d_eng already defined)
(label b47a_esp already defined)
(label b47a_eng already defined)
(label b37_esp already defined)
(label b37_eng already defined)
(label b32_esp already defined)
(label b32_eng already defined)
(label b21a_esp already defined)
(label b21a_eng already defined)
(label b21_esp already defined)
(label b21_eng already defined)
(label b18_esp already defined)
(label b18_eng already defined)
(label b13_esp already defined)
(label b13_eng already defined)
(label b12_esp already defined)
(label b12_eng already defined)
(label b43_esp already defined)
(label b43_eng already defined)
(label b6_esp already defined)
(label b6_eng already defined)
(label b4_esp already defined)
(label b4_eng already defined)
(label b3_esp already defined)
(label b3_eng already defined)
(label b2_esp already defined)
(label b2_eng already defined)
(label b1_esp already defined)
(label b1_eng already defined)
(label aoj12_esp already defined)
(label aoj12_eng already defined)
(label aoj11_esp already defined)
(label aoj11_eng already defined)
(label vic1exta_esp already defined)
(label vic1exta_eng already defined)
(label vic1ext_esp already defined)
(label vic1ext_eng already defined)
(label jc15a_esp already defined)
(label jc15a_eng already defined)
(label jc13_esp already defined)
(label jc13_eng already defined)
(label jc10_esp already defined)
(label jc10_eng already defined)
(label prot3_esp already defined)
(label prot3_eng already defined)
(label l1_esp already defined)
(label l1_eng already defined)
(label it1_esp already defined)
(label it1_eng already defined)
(label cp20_esp already defined)
(label cp20_eng already defined)
(label cp13_esp already defined)
(label cp13_eng already defined)
(label cp8_esp already defined)
(label cp8_eng already defined)
(label cp7_esp already defined)
(label cp7_eng already defined)
(label cp6_esp already defined)
(label cp6_eng already defined)
(label np1_esp already defined)
(label np1_eng already defined)
(label idio2_esp already defined)
(label idio2_eng already defined)
(label soct2_esp already defined)
(label soct2_eng already defined)
(label a4_esp already defined)
(label a4_eng already defined)
(label ls3_esp already defined)
(label ls3_eng already defined)
(label q1_esp already defined)
(label q1_eng already defined)
(label ur_esp already defined)
(label ur_eng already defined)
(label tamano_esp already defined)
(label tamano_eng already defined)
(label prov_esp already defined)
(label prov_eng already defined)
(label pais_esp already defined)
(label pais_eng already defined)
(label municipio_esp already defined)
(label municipio_eng already defined)
(label estratosec_esp already defined)
(label estratosec_eng already defined)
(label estratopri_esp already defined)
(label estratopri_eng already defined)

. append using "honduras.dta"
(label sex_esp already defined)
(label sex_eng already defined)
(label uniq_id_esp already defined)
(label uniq_id_eng already defined)
(label idiomaq_esp already defined)
(label idiomaq_eng already defined)
(label env2b_esp already defined)
(label env2b_eng already defined)
(label env1c_esp already defined)
(label env1c_eng already defined)
(label drk1_esp already defined)
(label drk1_eng already defined)
(label dst1b_esp already defined)
(label dst1b_eng already defined)
(label mil10un_esp already defined)
(label mil10un_eng already defined)
(label mil10oas_esp already defined)
(label mil10oas_eng already defined)
(label mil10e_esp already defined)
(label mil10e_eng already defined)
(label mil10a_esp already defined)
(label mil10a_eng already defined)
(label nationality_esp already defined)
(label nationality_eng already defined)
(label sexi_esp already defined)
(label sexi_eng already defined)
(label iarea7_esp already defined)
(label iarea7_eng already defined)
(label iarea6_esp already defined)
(label iarea6_eng already defined)
(label iarea5_esp already defined)
(label iarea5_eng already defined)
(label iarea4_esp already defined)
(label iarea4_eng already defined)
(label iarea3_esp already defined)
(label iarea3_eng already defined)
(label iarea2_esp already defined)
(label iarea2_eng already defined)
(label iarea1_esp already defined)
(label iarea1_eng already defined)
(label conocim_esp already defined)
(label conocim_eng already defined)
(label colorr_esp already defined)
(label colorr_eng already defined)
(label formatq_esp already defined)
(label formatq_eng already defined)
(label r16_esp already defined)
(label r16_eng already defined)
(label r1_esp already defined)
(label r1_eng already defined)
(label r18_esp already defined)
(label r18_eng already defined)
(label r15_esp already defined)
(label r15_eng already defined)
(label r14_esp already defined)
(label r14_eng already defined)
(label r12_esp already defined)
(label r12_eng already defined)
(label r8_esp already defined)
(label r8_eng already defined)
(label r7_esp already defined)
(label r7_eng already defined)
(label r6_esp already defined)
(label r6_eng already defined)
(label r5_esp already defined)
(label r5_eng already defined)
(label r4a_esp already defined)
(label r4a_eng already defined)
(label r4_esp already defined)
(label r4_eng already defined)
(label r3_esp already defined)
(label r3_eng already defined)
(label pr1_esp already defined)
(label pr1_eng already defined)
(label gi0_esp already defined)
(label gi0_eng already defined)
(label www1_esp already defined)
(label www1_eng already defined)
(label etid_esp already defined)
(label etid_eng already defined)
(label q12f_esp already defined)
(label q12f_eng already defined)
(label q12m_esp already defined)
(label q12m_eng already defined)
(label q12_esp already defined)
(label q12_eng already defined)
(label q12bn_esp already defined)
(label q12bn_eng already defined)
(label q12c_esp already defined)
(label q12c_eng already defined)
(label q11n_esp already defined)
(label q11n_eng already defined)
(label q10e_esp already defined)
(label q10e_eng already defined)
(label q10d_esp already defined)
(label q10d_eng already defined)
(label q14a_esp already defined)
(label q14a_eng already defined)
(label q14_esp already defined)
(label q14_eng already defined)
(label q10a_esp already defined)
(label q10a_eng already defined)
(label q10new_esp already defined)
(label q10new_eng already defined)
(label q10g_esp already defined)
(label q10g_eng already defined)
(label ocup1a_esp already defined)
(label ocup1a_eng already defined)
(label ocup4a_esp already defined)
(label ocup4a_eng already defined)
(label q3c_esp already defined)
(label q3c_eng already defined)
(label q5b_esp already defined)
(label q5b_eng already defined)
(label q5a_esp already defined)
(label q5a_eng already defined)
(label ed2_esp already defined)
(label ed2_eng already defined)
(label ed_esp already defined)
(label ed_eng already defined)
(label cct1b_esp already defined)
(label cct1b_eng already defined)
(label wf1_esp already defined)
(label wf1_eng already defined)
(label dvw2_esp already defined)
(label dvw2_eng already defined)
(label dvw1_esp already defined)
(label dvw1_eng already defined)
(label exp_b_esp already defined)
(label exp_b_eng already defined)
(label for5_esp already defined)
(label for5_eng already defined)
(label vb20_esp already defined)
(label vb20_eng already defined)
(label pol1_esp already defined)
(label pol1_eng already defined)
(label vb11_esp already defined)
(label vb11_eng already defined)
(label vb10_esp already defined)
(label vb10_eng already defined)
(label vb3n_esp already defined)
(label vb3n_eng already defined)
(label vb2_esp already defined)
(label vb2_eng already defined)
(label vb1_esp already defined)
(label vb1_eng already defined)
(label fear6f_esp already defined)
(label fear6f_eng already defined)
(label vic44_esp already defined)
(label vic44_eng already defined)
(label igaaoj22_esp already defined)
(label igaaoj22_eng already defined)
(label iga1_esp already defined)
(label iga1_eng already defined)
(label capital1_esp already defined)
(label capital1_eng already defined)
(label fear11_esp already defined)
(label fear11_eng already defined)
(label vicbar7f_esp already defined)
(label vicbar7f_eng already defined)
(label vicbar7_esp already defined)
(label vicbar7_eng already defined)
(label vicbarf_esp already defined)
(label vicbarf_eng already defined)
(label vicbar4f_esp already defined)
(label vicbar4f_eng already defined)
(label vicbar4_esp already defined)
(label vicbar4_eng already defined)
(label vicbar3f_esp already defined)
(label vicbar3f_eng already defined)
(label vicbar3_esp already defined)
(label vicbar3_eng already defined)
(label vicbar1f_esp already defined)
(label vicbar1f_eng already defined)
(label vicbar1_esp already defined)
(label vicbar1_eng already defined)
(label exc7new_esp already defined)
(label exc7new_eng already defined)
(label exc18_esp already defined)
(label exc18_eng already defined)
(label exc16_esp already defined)
(label exc16_eng already defined)
(label exc15_esp already defined)
(label exc15_eng already defined)
(label exc14_esp already defined)
(label exc14_eng already defined)
(label exc13_esp already defined)
(label exc13_eng already defined)
(label exc11_esp already defined)
(label exc11_eng already defined)
(label exc20_esp already defined)
(label exc20_eng already defined)
(label exc6_esp already defined)
(label exc6_eng already defined)
(label exc2_esp already defined)
(label exc2_eng already defined)
(label lib4_esp already defined)
(label lib4_eng already defined)
(label lib2c_esp already defined)
(label lib2c_eng already defined)
(label lib2b_esp already defined)
(label lib2b_eng already defined)
(label lib1_esp already defined)
(label lib1_eng already defined)
(label d6_esp already defined)
(label d6_eng already defined)
(label d5_esp already defined)
(label d5_eng already defined)
(label d4_esp already defined)
(label d4_eng already defined)
(label d3_esp already defined)
(label d3_eng already defined)
(label d2_esp already defined)
(label d2_eng already defined)
(label d1_esp already defined)
(label d1_eng already defined)
(label e16_esp already defined)
(label e16_eng already defined)
(label e15_esp already defined)
(label e15_eng already defined)
(label e5_esp already defined)
(label e5_eng already defined)
(label w14a_esp already defined)
(label w14a_eng already defined)
(label pn4_esp already defined)
(label pn4_eng already defined)
(label exp_a_esp already defined)
(label exp_a_eng already defined)
(label media4_esp already defined)
(label media4_eng already defined)
(label media3_esp already defined)
(label media3_eng already defined)
(label aoj22new_esp already defined)
(label aoj22new_eng already defined)
(label eff2_esp already defined)
(label eff2_eng already defined)
(label eff1_esp already defined)
(label eff1_eng already defined)
(label ing4_esp already defined)
(label ing4_eng already defined)
(label ros4_esp already defined)
(label ros4_eng already defined)
(label ros1_esp already defined)
(label ros1_eng already defined)
(label infra3_esp already defined)
(label infra3_eng already defined)
(label infrax_esp already defined)
(label infrax_eng already defined)
(label sd6new2_esp already defined)
(label sd6new2_eng already defined)
(label sd3new2_esp already defined)
(label sd3new2_eng already defined)
(label sd2new2_esp already defined)
(label sd2new2_eng already defined)
(label m2_esp already defined)
(label m2_eng already defined)
(label m1_esp already defined)
(label m1_eng already defined)
(label pr3e_esp already defined)
(label pr3e_eng already defined)
(label b15_esp already defined)
(label b15_eng already defined)
(label b47a_esp already defined)
(label b47a_eng already defined)
(label b37_esp already defined)
(label b37_eng already defined)
(label b32_esp already defined)
(label b32_eng already defined)
(label b21a_esp already defined)
(label b21a_eng already defined)
(label b21_esp already defined)
(label b21_eng already defined)
(label b20_esp already defined)
(label b20_eng already defined)
(label b18_esp already defined)
(label b18_eng already defined)
(label b13_esp already defined)
(label b13_eng already defined)
(label b12_esp already defined)
(label b12_eng already defined)
(label b43_esp already defined)
(label b43_eng already defined)
(label b11_esp already defined)
(label b11_eng already defined)
(label b6_esp already defined)
(label b6_eng already defined)
(label b4_esp already defined)
(label b4_eng already defined)
(label b3_esp already defined)
(label b3_eng already defined)
(label b2_esp already defined)
(label b2_eng already defined)
(label b1_esp already defined)
(label b1_eng already defined)
(label aoj12_esp already defined)
(label aoj12_eng already defined)
(label aoj17_esp already defined)
(label aoj17_eng already defined)
(label pese2_esp already defined)
(label pese2_eng already defined)
(label pese1_esp already defined)
(label pese1_eng already defined)
(label aoj11_esp already defined)
(label aoj11_eng already defined)
(label pole2nn_esp already defined)
(label pole2nn_eng already defined)
(label ico2_esp already defined)
(label ico2_eng already defined)
(label vic45n_esp already defined)
(label vic45n_eng already defined)
(label vic43_esp already defined)
(label vic43_eng already defined)
(label vic41_esp already defined)
(label vic41_eng already defined)
(label vic74_esp already defined)
(label vic74_eng already defined)
(label vic40a_esp already defined)
(label vic40a_eng already defined)
(label vic73_esp already defined)
(label vic73_eng already defined)
(label vic72_esp already defined)
(label vic72_eng already defined)
(label vic71_esp already defined)
(label vic71_eng already defined)
(label arm2_esp already defined)
(label arm2_eng already defined)
(label vic1hogar_esp already defined)
(label vic1hogar_eng already defined)
(label vic1exta_esp already defined)
(label vic1exta_eng already defined)
(label vic1ext_esp already defined)
(label vic1ext_eng already defined)
(label jc15a_esp already defined)
(label jc15a_eng already defined)
(label jc13_esp already defined)
(label jc13_eng already defined)
(label jc10_esp already defined)
(label jc10_eng already defined)
(label prot3_esp already defined)
(label prot3_eng already defined)
(label l1_esp already defined)
(label l1_eng already defined)
(label it1_esp already defined)
(label it1_eng already defined)
(label cp20_esp already defined)
(label cp20_eng already defined)
(label cp13_esp already defined)
(label cp13_eng already defined)
(label cp8_esp already defined)
(label cp8_eng already defined)
(label cp7_esp already defined)
(label cp7_eng already defined)
(label cp6_esp already defined)
(label cp6_eng already defined)
(label sgl1_esp already defined)
(label sgl1_eng already defined)
(label np1_esp already defined)
(label np1_eng already defined)
(label idio2_esp already defined)
(label idio2_eng already defined)
(label soct2_esp already defined)
(label soct2_eng already defined)
(label a4_esp already defined)
(label a4_eng already defined)
(label ls3_esp already defined)
(label ls3_eng already defined)
(label q1_esp already defined)
(label q1_eng already defined)
(label tamano_esp already defined)
(label tamano_eng already defined)
(label ur_esp already defined)
(label ur_eng already defined)
(label municipio_esp already defined)
(label municipio_eng already defined)
(label prov_esp already defined)
(label prov_eng already defined)
(label estratosec_esp already defined)
(label estratosec_eng already defined)
(label estratopri_esp already defined)
(label estratopri_eng already defined)
(label pais_esp already defined)
(label pais_eng already defined)

. save "bateson_weintraub.dta",replace
file bateson_weintraub.dta saved

. 
. 
. //Label, create, and clean key variables
. 
. //Label country variable
. 
. label define pais 3 "El Salvador" 4 "Honduras" 12 "Paraguay" 21 "Dominican Re
> public"

. label values pais pais

. 
. //Establish TREATMENT variable: 1 = after election; 0 = interviewed on Electi
> on Day or before. 
. //Note: the US presidential election was held on Nov. 8, 2016 (date=20766), s
> o Nov. 9 (date=20767) is the first day after the election. Dates 20767 and gr
> eater are coded post-Trump.
. gen posttrump = 0

. replace posttrump= 1 if fecha>=20767
(2,799 real changes made)

. label var posttrump "Post-Trump, All Dates"

. 
. //Establish varying bandwidths for TREATMENT variable
. // The naive version compares ALL post-election interviewees with ALL pre-ele
> ction interviewees, with no bandwidth restrictions.
. gen treatment_naive = . 
(6,157 missing values generated)

. replace treatment_naive = posttrump
(6,157 real changes made)

. label var treatment_naive "Post-Trump, All Dates"

. 
. * Different bandwidths
. //Note: These bandwidths compare respondents contacted within X days after el
> ection vs. those contacted within X days before the election.
. 
. * 21 Day Bandwidth
. gen posttrump_21days=.
(6,157 missing values generated)

. replace posttrump_21days = 1 if fecha >=20767 & fecha<=20787
(2,706 real changes made)

. replace posttrump_21days = 0 if fecha< 20767 & fecha>=20746
(3,214 real changes made)

. label var posttrump_21days "Post-Trump 21 Day Window"

. 
. * 20 Day Bandwidth
. gen posttrump_20days=.
(6,157 missing values generated)

. replace posttrump_20days = 1 if fecha >=20767 & fecha<=20786
(2,665 real changes made)

. replace posttrump_20days = 0 if fecha< 20767 & fecha>=20747
(3,146 real changes made)

. label var posttrump_20days "Post-Trump 20 Day Window"

. 
. * 19 Day Bandwidth
. gen posttrump_19days=.
(6,157 missing values generated)

. replace posttrump_19days = 1 if fecha >=20767 & fecha<=20785
(2,625 real changes made)

. replace posttrump_19days = 0 if fecha< 20767 & fecha>=20748
(3,078 real changes made)

. label var posttrump_19days "Post-Trump 19 Day Window"

. 
. * 18 Day Bandwidth
. gen posttrump_18days=.
(6,157 missing values generated)

. replace posttrump_18days = 1 if fecha >=20767 & fecha<=20784
(2,559 real changes made)

. replace posttrump_18days = 0 if fecha< 20767 & fecha>=20749
(2,999 real changes made)

. label var posttrump_18days "Post-Trump 18 Day Window"

. 
. * 17 Day Bandwidth
. gen posttrump_17days=.
(6,157 missing values generated)

. replace posttrump_17days = 1 if fecha >=20767 & fecha<=20783
(2,488 real changes made)

. replace posttrump_17days = 0 if fecha< 20767 & fecha>=20750
(2,881 real changes made)

. label var posttrump_17days "Post-Trump 17 Day Window"

. 
. * 16 Day Bandwidth
. gen posttrump_16days=.
(6,157 missing values generated)

. replace posttrump_16days = 1 if fecha >=20767 & fecha<=20782
(2,392 real changes made)

. replace posttrump_16days = 0 if fecha< 20767 & fecha>=20751
(2,768 real changes made)

. label var posttrump_16days "Post-Trump 16 Day Window"

. 
. * 15 Day Bandwidth
. gen posttrump_15days=.
(6,157 missing values generated)

. replace posttrump_15days = 1 if fecha >=20767 & fecha<=20781
(2,282 real changes made)

. replace posttrump_15days = 0 if fecha< 20767 & fecha>=20752
(2,643 real changes made)

. label var posttrump_15days "Post-Trump 15 Day Window"

. 
. * 14 Day Bandwidth
. gen posttrump_14days=.
(6,157 missing values generated)

. replace posttrump_14days = 1 if fecha >=20767 & fecha<=20780
(2,182 real changes made)

. replace posttrump_14days = 0 if fecha< 20767 & fecha>=20753
(2,555 real changes made)

. label var posttrump_14days "Post-Trump 14 Day Window"

. 
. *13 Day Bandwidth
. gen posttrump_13days=.
(6,157 missing values generated)

. replace posttrump_13days = 1 if fecha >=20767 & fecha<=20779
(2,049 real changes made)

. replace posttrump_13days = 0 if fecha< 20767 & fecha>=20754
(2,420 real changes made)

. label var posttrump_13days "Post-Trump 13 Day Window"

. 
. *12 Day Bandwidth
. gen posttrump_12days=.
(6,157 missing values generated)

. replace posttrump_12days = 1 if fecha >=20767 & fecha<=20778
(1,931 real changes made)

. replace posttrump_12days = 0 if fecha< 20767 & fecha>=20755
(2,241 real changes made)

. label var posttrump_12days "Post-Trump 12 Day Window"

. 
. *11 Day Bandwidth
. gen posttrump_11days=.
(6,157 missing values generated)

. replace posttrump_11days = 1 if fecha >=20767 & fecha<=20777
(1,862 real changes made)

. replace posttrump_11days = 0 if fecha< 20767 & fecha>=20756
(2,053 real changes made)

. label var posttrump_11days "Post-Trump 11 Day Window"

. 
. *10 Day Bandwidth
. gen posttrump_10days=.
(6,157 missing values generated)

. replace posttrump_10days = 1 if fecha >=20767 & fecha<=20776
(1,768 real changes made)

. replace posttrump_10days = 0 if fecha< 20767 & fecha>=20757
(1,851 real changes made)

. label var posttrump_10days "Post-Trump 10 Day Window"

. 
. *9 Day Bandwidth
. gen posttrump_9days=.
(6,157 missing values generated)

. replace posttrump_9days = 1 if fecha >=20767 & fecha<=20775
(1,644 real changes made)

. replace posttrump_9days = 0 if fecha< 20767 & fecha>=20758
(1,708 real changes made)

. label var posttrump_9days "Post-Trump 9 Day Window"

. 
. *8 Day Bandwidth
. gen posttrump_8days=.
(6,157 missing values generated)

. replace posttrump_8days = 1 if fecha >=20767 & fecha<=20774
(1,479 real changes made)

. replace posttrump_8days = 0 if fecha< 20767 & fecha>=20759
(1,551 real changes made)

. label var posttrump_8days "Post-Trump 8 Day Window"

. 
. *7 Day Bandwidth
. gen posttrump_7days=.
(6,157 missing values generated)

. replace posttrump_7days = 1 if fecha >=20767 & fecha<=20773
(1,304 real changes made)

. replace posttrump_7days = 0 if fecha< 20767 & fecha>=20760
(1,355 real changes made)

. label var posttrump_7days "Post-Trump 7 Day Window"

. 
. *6 Day Bandwidth
. gen posttrump_6days=.
(6,157 missing values generated)

. replace posttrump_6days = 1 if fecha >=20767 & fecha<=20772
(1,153 real changes made)

. replace posttrump_6days = 0 if fecha <20767 & fecha >=20761
(1,221 real changes made)

. label var posttrump_6days "Post-Trump 6 Day Window"

. 
. *5 Day Bandwidth
. gen posttrump_5days=.
(6,157 missing values generated)

. replace posttrump_5days = 1 if fecha >=20767 & fecha<=20771
(967 real changes made)

. replace posttrump_5days = 0 if fecha< 20767 & fecha>=20762
(1,034 real changes made)

. label var posttrump_5days "Post-Trump 5 Day Window"

. 
. *4 Day Bandwidth
. gen posttrump_4days=.
(6,157 missing values generated)

. replace posttrump_4days = 1 if fecha>=20767 & fecha<=20770
(746 real changes made)

. replace posttrump_4days = 0 if fecha< 20767 & fecha>=20763
(777 real changes made)

. label var posttrump_4days "Post-Trump 4 Day Window"

. 
. *3 Day Bandwidth
. gen posttrump_3days=.
(6,157 missing values generated)

. replace posttrump_3days = 1 if fecha>=20767 & fecha<=20769
(546 real changes made)

. replace posttrump_3days = 0 if fecha< 20767 & fecha>=20764
(526 real changes made)

. label var posttrump_3days "Post-Trump 3 Day Window"

. 
. *2 Day Bandwidth
. gen posttrump_2days=.
(6,157 missing values generated)

. replace posttrump_2days = 1 if fecha>=20767 & fecha<=20768
(383 real changes made)

. replace posttrump_2days = 0 if fecha< 20767 & fecha>=20765
(338 real changes made)

. label var posttrump_2days "Post-Trump 2 Day Window"

. 
. //Set up DEPENDENT VARIABLE//
. 
. *Recode our main dependent variable, such that larger numbers indicate more t
> rust in the US Government
. //Note: in the original LAPOP data, 4=untrustworthy and 1=very trustworthy. W
> e've recoded the variable such that 4=very trustworthy and 1=untrustworthy.
. rename mil10e trustusgov

. recode trustusgov 1=5
(trustusgov: 1146 changes made)

. recode trustusgov 4=1
(trustusgov: 351 changes made)

. recode trustusgov 5=4
(trustusgov: 1146 changes made)

. recode trustusgov 2=5
(trustusgov: 1585 changes made)

. recode trustusgov 3=2
(trustusgov: 903 changes made)

. recode trustusgov 5=3
(trustusgov: 1585 changes made)

. 
. label define trustusgov 1 "Untrustworthy" 2 "Not Very Trustworthy" 3 "Somewha
> t Trustworthy" 4 "Very Trustworthy"

. label values trustusgov trustusgov

. 
. *Create dummy variables indicating who responded "Don't Know" re: trust in US
>  gov, and who is coded as "No Response"
. 
. gen trustusgovdk=0

. replace trustusgovdk=1 if trustusgov==.a
(2,071 real changes made)

. label define trustusgovdk 1 "Doesn't Know if Trusts US Gov" 0 "Did Not Say Do
> esn't Know if Trusts US Gov"

. label values trustusgovdk trustusgovdk

. 
. gen trustusgovnr=0

. replace trustusgovnr=1 if trustusgov==.b
(101 real changes made)

. label define trustusgovnr 1 "No Response to Trust US Gov Question" 0 "Respond
> ed to Trust US Gov Question"

. label values trustusgovnr trustusgovnr

. 
. //Note: Some of our models use a binary version of the dependent variable. 
. //This is coded 1 if the respondent says USG is very or somewhat trusthworthy
> ; 0 if they say the USG is not very trustworthy or untrustworthy.
. 
. *Create binary dependent variable
. 
. gen dummytrustusg=.
(6,157 missing values generated)

. replace dummytrustusg=0 if trustusgov==1
(351 real changes made)

. replace dummytrustusg=0 if trustusgov==2
(903 real changes made)

. replace dummytrustusg=1 if trustusgov==3 
(1,585 real changes made)

. replace dummytrustusg=1 if trustusgov==4
(1,146 real changes made)

. label define dummytrustusg 1 "Trusts US Gov" 0 "Does Not Trust US Gov"

. label values dummytrustusg dummytrustusg 

. 
. //Rename and recode CONTROL variables//
. 
. rename vb2 voted_lastpresidential

. recode voted_lastpresidential 2=0
(voted_lastpresidential: 1771 changes made)

. rename vb1 voteregistered

. recode voteregistered 2=0
(voteregistered: 499 changes made)

. recode voteregistered 3=.
(voteregistered: 77 changes made)

. rename ed education

. rename ed2 education_mother

. rename ocup4a employment

. gen working =employment if employment== 1
(3,540 missing values generated)

. replace working = 0 if working==.
(3,540 real changes made)

. rename q10new householdincome

. rename q1 male

. recode male 2=0
(male: 3092 changes made)

. rename q2 age

. rename q10a remesas

. recode remesas 2=0
(remesas: 4894 changes made)

. rename q14 emigrate

. recode emigrate 2=0
(emigrate: 3873 changes made)

. rename tamano citysize

. //Note: In the manuscript, we refer to "citysize" as "Size of Location". It i
> s a 5-point scale: larger numbers are more rural. 1 = Capital metro area and 
> 5 = rural area.
. 
. //create citysize dummies for use in some models//
. gen capital=0

. replace capital=1 if citysize==1
(1,424 real changes made)

. gen largecity=0

. replace largecity=1 if citysize==2
(719 real changes made)

. gen smallcity=0

. replace smallcity=1 if citysize==3
(1,223 real changes made)

. gen town=0

. replace town=1 if citysize==4
(694 real changes made)

. gen rural=0

. replace rural=1 if citysize==5
(2,097 real changes made)

. 
. rename l1 ideology

. //Note: lower numbers = more left/liberal; higher numbers = more conservative
> /right.
. 
. rename pais country

. 
. //create country dummies//
. gen elsalv=0

. replace elsalv=1 if country==3
(1,551 real changes made)

. gen honduras=0

. replace honduras=1 if country==4
(1,560 real changes made)

. gen dr=0

. replace dr=1 if country==21
(1,518 real changes made)

. gen paraguay=0

. replace paraguay=1 if country==12 
(1,528 real changes made)

. 
. //Rename and recode additional dependent variables for PLACEBO TESTS: these v
> ariables measure trust in other foreign entities
. 
. //Trust in CHINESE GOV
. //Recode dependent variable so that larger numbers mean more trust in the Chi
> nese government
. rename mil10a trustchina

. recode trustchina 1=5
(trustchina: 545 changes made)

. recode trustchina 4=1
(trustchina: 318 changes made)

. recode trustchina 5=4
(trustchina: 545 changes made)

. recode trustchina 2=5
(trustchina: 859 changes made)

. recode trustchina 3=2
(trustchina: 543 changes made)

. recode trustchina 5=3
(trustchina: 859 changes made)

. 
. label define trustchina 1 "Untrustworthy" 2 "Not Very Trustworthy" 3 "Somewha
> t Trustworthy" 4 "Very Trustworthy"

. label values trustchina trustchina

. 
. gen trustchinadk=0

. replace trustchinadk=1 if trustchina==.a
(3,786 real changes made)

. label define trustchinadk 1 "Doesn't Know if Trusts Chinese Gov" 0 "Did Not S
> ay Doesn't Know if Trusts Chinese Gov"

. label values trustchinadk trustchinadk

. 
. gen trustchinanr=0

. replace trustchinanr=1 if trustchina==.b
(106 real changes made)

. label define trustchinanr 1 "No Response to Trust Chinese Gov Question" 0 "Re
> sponded to Trust Chinese Gov Question"

. label values trustchinanr trustchinanr

. 
. //Create dummy variable measuring trust in Chinese gov
. gen dummytrustchina=.
(6,157 missing values generated)

. replace dummytrustchina=0 if trustchina==1
(318 real changes made)

. replace dummytrustchina=0 if trustchina==2
(543 real changes made)

. replace dummytrustchina=1 if trustchina==3
(859 real changes made)

. replace dummytrustchina=1 if trustchina==4
(545 real changes made)

. 
. //Note: the trust in Chinese gov question had a high rate of "Don't Know" res
> ponses.
. //The variables above allow us to analyze the "No response" and "don't know" 
> answers to the question.
. //There were 3,765 "Don't Know" answers and 106 answers coded "No Response."
. 
. //Trust in UN
. //Recode variable so that larger numbers mean more trust in the UN.
. rename mil10un trustun

. recode trustun 1=5
(trustun: 932 changes made)

. recode trustun 4=1
(trustun: 234 changes made)

. recode trustun 5=4
(trustun: 932 changes made)

. recode trustun 2=5
(trustun: 1540 changes made)

. recode trustun 3=2
(trustun: 829 changes made)

. recode trustun 5=3
(trustun: 1540 changes made)

. 
. label define trustun 1 "Untrustworthy" 2 "Not Very Trustworthy" 3 "Somewhat T
> rustworthy" 4 "Very Trustworthy"

. label values trustun trustun

. 
. gen trustundk=0

. replace trustundk=1 if trustun==.a
(2,517 real changes made)

. label define trustundk 1 "Doesn't Know if Trusts UN" 0 "Did Not Say Doesn't K
> now if Trusts UN"

. label values trustundk trustundk

. 
. gen trustunnr=0

. replace trustunnr=1 if trustun==.b
(105 real changes made)

. label define trustunnr 1 "No Response to Trust UN Question" 0 "Responded to T
> rust UN Question"

. label values trustunnr trustunnr

. 
. gen dummytrustun=.
(6,157 missing values generated)

. replace dummytrustun=0 if trustun==1
(234 real changes made)

. replace dummytrustun=0 if trustun==2
(829 real changes made)

. replace dummytrustun=1 if trustun==3
(1,540 real changes made)

. replace dummytrustun=1 if trustun==4
(932 real changes made)

. 
. //Trust in OAS
. //Recode variable so larger numbers mean more trust in the OAS
. rename mil10oas trustoas

. recode trustoas 1=5
(trustoas: 812 changes made)

. recode trustoas 4=1
(trustoas: 259 changes made)

. recode trustoas 5=4
(trustoas: 812 changes made)

. recode trustoas 2=5
(trustoas: 1521 changes made)

. recode trustoas 3=2
(trustoas: 862 changes made)

. recode trustoas 5=3
(trustoas: 1521 changes made)

. 
. label define trustoas 1 "Untrustworthy" 2 "Not Very Trustworthy" 3 "Somewhat 
> Trustworthy" 4 "Very Trustworthy"

. label values trustoas trustoas

. 
. gen trustoasdk=0

. replace trustoasdk=1 if trustoas==.a
(2,599 real changes made)

. label define trustoasdk 1 "Doesn't Know if Trusts OAS" 0 "Did Not Say Doesn't
>  Know if Trusts OAS"

. label values trustoasdk trustoasdk

. 
. gen trustoasnr=0

. replace trustoasnr=1 if trustoas==.b
(104 real changes made)

. label define trustoasnr 1 "No Response to Trust OAS Question" 0 "Responded to
>  Trust OAS Question"

. label values trustoasnr trustoasnr 

. 
. gen dummytrustoas=.
(6,157 missing values generated)

. replace dummytrustoas=0 if trustoas==1
(259 real changes made)

. replace dummytrustoas=0 if trustoas==2
(862 real changes made)

. replace dummytrustoas=1 if trustoas==3
(1,521 real changes made)

. replace dummytrustoas=1 if trustoas==4
(812 real changes made)

. 
. //CREATE TIME VARIABLE
. 
. //Following the recommendations of Munoz et al 2020, we create a time variabl
> e, allowing for an interaction term in subsequent analyses
. //Nov. 9, 2016 is coded as "zero" for all countries.The variable then counts 
> backward and forward from there.
. 
. gen time_zero = fecha-20767

. label var time_zero "Days"

. 
. //ENTROPY BALANCING
. 
. //This section uses ENTROPY BALANCING to ensure similar treatment and control
>  units, using a 7-day window pre & post-election
. //We use entropy balancing to preprocess data for our binary treatment, in li
> ne with Muñoz et al (2020)
. 
. //Install the ebalance package from Hainmueller and Xu (2013). See https://we
> b.stanford.edu/~jhain/Paper/JSS2013.pdf
. 
. ssc install ebalance
checking ebalance consistency and verifying not already installed...
all files already exist and are up to date.

. 
. //Note that the entropy balancing does not consider country or size of locati
> on, because these variables are used for fixed effects in our regressions. 
. 
. ebalance posttrump_7days male age householdincome education working voted_las
> tpresidential voteregistered, generate(balance_seven) targets(3)


Data Setup
Treatment variable:   posttrump_7days
Covariate adjustment: male age householdincome education working voted_lastpres
> idential voteregistered (1st order). male age householdincome education worki
> ng voted_lastpresidential voteregistered (2nd order). male age householdincom
> e education working voted_lastpresidential voteregistered (3rd order).


Optimizing...
Iteration 1: Max Difference = 20614.0682
Iteration 2: Max Difference = 7582.51557
Iteration 3: Max Difference = 2788.4703
Iteration 4: Max Difference = 1024.82612
Iteration 5: Max Difference = 375.981421
Iteration 6: Max Difference = 137.189046
Iteration 7: Max Difference = 49.1037193
Iteration 8: Max Difference = 16.2157825
Iteration 9: Max Difference = 3.77853546
Iteration 10: Max Difference = .27765159
Iteration 11: Max Difference = .002055498
maximum difference smaller than the tolerance level; convergence achieved


Treated units: 1047    total of weights: 1047
Control units: 1070    total of weights: 1047


Before: without weighting

             |              Treat              |             Control           
>   
             |      mean   variance   skewness |      mean   variance   skewnes
> s 
-------------+---------------------------------+-------------------------------
--
        male |     .5081      .2502    -.03248 |     .5103      .2501    -.0411
> 3 
         age |     38.77      243.1      .6991 |     38.86      248.3      .749
> 1 
householdi~e |     7.089      25.69      .2961 |     6.916      24.98      .309
> 8 
   education |     8.968      19.99     -.0412 |     8.802       18.6     -.124
> 3 
     working |     .4317      .2456      .2757 |     .4374      .2463      .252
> 5 
voted_last~l |     .7459      .1897      -1.13 |     .7131      .2048     -.942
> 2 
voteregist~d |     .9427     .05407     -3.809 |     .9131     .07944     -2.93
> 3 


After:  balance_seven as the weighting variable

             |              Treat              |             Control           
>   
             |      mean   variance   skewness |      mean   variance   skewnes
> s 
-------------+---------------------------------+-------------------------------
--
        male |     .5081      .2502    -.03248 |     .5081      .2502    -.0324
> 8 
         age |     38.77      243.1      .6991 |     38.77      243.1      .699
> 2 
householdi~e |     7.089      25.69      .2961 |     7.089      25.69      .296
> 1 
   education |     8.968      19.99     -.0412 |     8.968      19.99    -.0411
> 9 
     working |     .4317      .2456      .2757 |     .4317      .2456      .275
> 7 
voted_last~l |     .7459      .1897      -1.13 |     .7459      .1897      -1.1
> 3 
voteregist~d |     .9427     .05407     -3.809 |     .9427     .05409     -3.80
> 9 

. 
. //Store entropy balancing descriptives into matrix to generate table
. matrix pre = e(preBal)

. matrix post = e(postBal)

. 
. //Generate variable that captures the units included in the entropy balancing
>  weights estimation
. gen sample_bal = e(sample)

. 
. * Apply generated weights
. svyset [pweight=balance_seven]

      pweight: balance_seven
          VCE: linearized
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>

. 
. 
. *************************************
. *************************************
. //2. BALANCE TESTS FOR FIGURE 1//
. *************************************
. *************************************
. 
. //Create Figure 1: Balance for full sample, 21 day, 14 day, and 7 day bandwid
> ths
. 
. clear matrix

. foreach var of varlist treatment_naive  posttrump_21days posttrump_14days pos
> ttrump_7days  {
  2.         gen r_`var' = `var'
  3.         recode r_`var' (1=0) (0=1)
  4. }
(r_treatment_naive: 6157 changes made)
(237 missing values generated)
(r_posttrump_21days: 5920 changes made)
(1,420 missing values generated)
(r_posttrump_14days: 4737 changes made)
(3,498 missing values generated)
(r_posttrump_7days: 2659 changes made)

. 
. // Conduct t-test and store results in matrix to generate the plot
. foreach tr of varlist  treatment_naive  posttrump_21days posttrump_14days pos
> ttrump_7days {
  2.         matrix mean = J(1,9,.)
  3.         matrix colnames mean =  male age householdincome citysize educatio
> n working voted_lastpresidential voteregistered remesas 
  4.         matrix CI = J(4,9,.)
  5.         matrix colnames CI =  male age householdincome citysize education 
> working  voted_lastpresidential voteregistered remesas 
  6.         matrix rownames CI = ll95 ul95 ll90 ul90
  7.         local i 0
  8.         foreach var of varlist  male age householdincome citysize educatio
> n working  voted_lastpresidential voteregistered remesas {
  9.                 quietly: ttest `var', by(`tr') 
 10.                 local ++ i 
 11.                 local diff =  r(mu_2) - r(mu_1) 
 12.                 matrix mean[1, `i'] = `diff' 
 13.                 local degrees = r(df_t)
 14.                 local critical_5 = invttail(`degrees', 0.025)
 15.                 local confvalue_5 = `critical_5' * r(se)
 16.                 local critical_10 = invttail(`degrees', 0.05)
 17.                 local confvalue_10 = `critical_10' * r(se)
 18.                 local ll95 = `diff' - `confvalue_5'
 19.                 local ul95 = `diff' + `confvalue_5'
 20.                 local ll90 = `diff' - `confvalue_10'
 21.                 local ul90 = `diff' + `confvalue_10'
 22.                 matrix CI[1, `i'] = `ll95' \ `ul95' \ `ll90' \ `ul90'
 23.         }
 24. matrix `tr'_m = mean
 25. matrix `tr'_CI = CI
 26. }

. 
. // Generate figure for balance tests 
. label var working "Working"

. label var voteregistered "Registered to Vote"

. label var voted_lastpresidential "Voted Last Presidential Election"

. label var remesas "Remittances"

. 
. coefplot (matrix(treatment_naive_m), xline(0) ci((treatment_naive_CI[1] treat
> ment_naive_CI[2]) (treatment_naive_CI[3] treatment_naive_CI[4]))) ///
>                 || (matrix(posttrump_21days_m), xline(0, lpattern(solid)) ci(
> (posttrump_21days_CI[1] posttrump_21days_CI[2]) (posttrump_21days_CI[3] postt
> rump_21days_CI[4]))) ///
>                 || (matrix(posttrump_14days_m), xline(0, lpattern(solid)) ci(
> (posttrump_14days_CI[1] posttrump_14days_CI[2]) (posttrump_14days_CI[3] postt
> rump_14days_CI[4]))) ///
>                 || (matrix(posttrump_7days_m), xline(0, lpattern(solid)) ci((
> posttrump_7days_CI[1] posttrump_7days_CI[2]) (posttrump_7days_CI[3] posttrump
> _7days_CI[4]))) ///
>                 , byopts(row(2)) xlabel(-1(.5)1) ylabel(, labsize(small)) xsc
> ale(range(-1 1)) xline(0, lpattern(solid))  ///
>                         nokey nooffset bylabels("Full sample" "± 21 days" "± 
> 14 days" "± 7 days") rescale(male remesas  working voted_lastpresidential vot
> eregistered=15) xtitle("Mean Difference Between Treatment and Control Groups 
> with 90% and 95% Confidence Intervals")

.         
.         
. graph save balancetests_reduced.gph, replace 
(file balancetests_reduced.gph saved)

. graph export balancetests_reduced.png, replace 
(file balancetests_reduced.png written in PNG format)

. 
. drop r_*

. 
. *Generate accompanying table, which is Appendix Table B.5. 
. 
. //Create balance table for 21 day window
. 
. foreach var of varlist male age householdincome citysize education working vo
> ted_lastpresidential voteregistered remesas{
  2.         
.         reg `var' posttrump_21days
  3.         global m`var'_0: di %6.3fc _b[_cons]
  4.         global m`var'_1: di %6.3fc _b[_cons] + _b[posttrump_21days]
  5.         global dif_`var': di %6.3fc _b[posttrump_21days]
  6. 
.         global lbe_`var' : var label `var'
  7. 
.         qui test posttrump_21days=0
  8.         global p_`var': di %12.3fc r(p)
  9.         glo star_`var'=cond(${p_`var'}<.001,"***",cond(${p_`var'}<.01,"**"
> ,cond(${p_`var'}<.05,"*",cond(${p_`var'}<.1,"+",""))))
 10. }

      Source |       SS           df       MS      Number of obs   =     5,920
-------------+----------------------------------   F(1, 5918)      =      0.28
       Model |   .06962756         1   .06962756   Prob > F        =    0.5978
    Residual |  1479.91669     5,918  .250070411   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  1479.98632     5,919  .250039925   Root MSE        =    .50007

-------------------------------------------------------------------------------
---
            male |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   .0068844   .0130468     0.53   0.598    -.0186922    .0324
> 609
           _cons |   .4953329   .0088208    56.16   0.000     .4780409    .5126
> 249
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,910
-------------+----------------------------------   F(1, 5908)      =      0.05
       Model |  12.8876749         1  12.8876749   Prob > F        =    0.8265
    Residual |  1585234.01     5,908  268.319906   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  1585246.89     5,909  268.276679   Root MSE        =     16.38

-------------------------------------------------------------------------------
---
             age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |  -.0937337   .4276959    -0.22   0.827    -.9321741    .7447
> 066
           _cons |    39.5708   .2892975   136.78   0.000     39.00368    40.13
> 793
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,968
-------------+----------------------------------   F(1, 4966)      =      4.21
       Model |  109.296255         1  109.296255   Prob > F        =    0.0401
    Residual |  128784.235     4,966  25.9331927   R-squared       =    0.0008
-------------+----------------------------------   Adj R-squared   =    0.0006
       Total |  128893.531     4,967  25.9499761   Root MSE        =    5.0925

-------------------------------------------------------------------------------
---
 householdincome |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   -.297917   .1451178    -2.05   0.040    -.5824119    -.013
> 422
           _cons |   7.605234   .0977694    77.79   0.000     7.413563    7.796
> 905
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,920
-------------+----------------------------------   F(1, 5918)      =    209.07
       Model |  485.887903         1  485.887903   Prob > F        =    0.0000
    Residual |  13753.4417     5,918  2.32400163   R-squared       =    0.0341
-------------+----------------------------------   Adj R-squared   =    0.0340
       Total |  14239.3296     5,919  2.40569852   Root MSE        =    1.5245

-------------------------------------------------------------------------------
---
        citysize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   .5750983   .0397734    14.46   0.000      .497128    .6530
> 686
           _cons |   3.010268   .0268903   111.95   0.000     2.957553    3.062
> 982
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,753
-------------+----------------------------------   F(1, 5751)      =      0.18
       Model |    3.466594         1    3.466594   Prob > F        =    0.6739
    Residual |   112596.24     5,751  19.5785498   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  112599.707     5,752  19.5757487   Root MSE        =    4.4248

-------------------------------------------------------------------------------
---
       education |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |  -.0492369   .1170118    -0.42   0.674    -.2786241    .1801
> 503
           _cons |   8.780614   .0795353   110.40   0.000     8.624695    8.936
> 533
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,920
-------------+----------------------------------   F(1, 5918)      =      0.21
       Model |   .05026065         1   .05026065   Prob > F        =    0.6502
    Residual |  1446.49957     5,918  .244423719   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  1446.54983     5,919  .244390916   Root MSE        =    .49439

-------------------------------------------------------------------------------
---
         working |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |  -.0058491   .0128987    -0.45   0.650    -.0311352    .0194
> 371
           _cons |   .4275047   .0087206    49.02   0.000      .410409    .4446
> 003
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,848
-------------+----------------------------------   F(1, 5846)      =      1.82
       Model |  .374210923         1  .374210923   Prob > F        =    0.1776
    Residual |  1203.34244     5,846  .205840309   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =    0.0001
       Total |  1203.71666     5,847  .205869105   Root MSE        =     .4537

-------------------------------------------------------------------------------
---
voted_lastpres~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   .0160604   .0119114     1.35   0.178    -.0072904    .0394
> 111
           _cons |   .7028302   .0080455    87.36   0.000     .6870581    .7186
> 023
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,828
-------------+----------------------------------   F(1, 5826)      =      6.25
       Model |  .484865812         1  .484865812   Prob > F        =    0.0124
    Residual |  451.642107     5,826  .077521817   R-squared       =    0.0011
-------------+----------------------------------   Adj R-squared   =    0.0009
       Total |  452.126973     5,827  .077591724   Root MSE        =    .27843

-------------------------------------------------------------------------------
---
  voteregistered |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   .0183056   .0073196     2.50   0.012     .0039565    .0326
> 547
           _cons |   .9068441   .0049561   182.97   0.000     .8971282      .91
> 656
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     5,899
-------------+----------------------------------   F(1, 5897)      =      2.60
       Model |  .419041562         1  .419041562   Prob > F        =    0.1071
    Residual |  951.311421     5,897  .161321252   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |  951.730463     5,898  .161364948   Root MSE        =    .40165

-------------------------------------------------------------------------------
---
         remesas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_21days |   .0169192   .0104978     1.61   0.107    -.0036603    .0374
> 986
           _cons |   .1945052   .0070969    27.41   0.000     .1805927    .2084
> 176
-------------------------------------------------------------------------------
---

. 
. //Output the results of the 21 day window balance test
.         texdoc init balance_table_21days.tex, replace force
(texdoc output file is balance_table_21days.tex)

.         tex \begin{tabular}{lccc} \toprule \toprule

.         tex Variable                    &       Mean Control    & Mean Treatm
> ent & Difference \\

.         tex \addlinespace \hline \\

.         foreach var of varlist male age householdincome citysize education wo
> rking voted_lastpresidential voteregistered remesas{
  2.         tex ${lbe_`var'} & ${m`var'_0} & ${m`var'_1} & ${dif_`var'}${star_
> `var'}\\
  3.         }

.         tex \hline \hline

.         tex \end{tabular}

. 
.         
. //Create balance table for 14 day window
. 
. foreach var of varlist male age householdincome citysize education working vo
> ted_lastpresidential voteregistered remesas{
  2.         
.         reg `var' posttrump_14days
  3.         global m`var'_0: di %6.3fc _b[_cons]
  4.         global m`var'_1: di %6.3fc _b[_cons] + _b[posttrump_14days]
  5.         global dif_`var': di %6.3fc _b[posttrump_14days]
  6. 
.         global lbe_`var' : var label `var'
  7. 
.         qui test posttrump_14days=0
  8.         global p_`var': di %12.3fc r(p)
  9.         glo star_`var'=cond(${p_`var'}<.001,"***",cond(${p_`var'}<.01,"**"
> ,cond(${p_`var'}<.05,"*",cond(${p_`var'}<.1,"+",""))))
 10. }

      Source |       SS           df       MS      Number of obs   =     4,737
-------------+----------------------------------   F(1, 4735)      =      0.06
       Model |  .014644786         1  .014644786   Prob > F        =    0.8088
    Residual |  1184.23404     4,735  .250102225   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  1184.24868     4,736  .250052509   Root MSE        =     .5001

-------------------------------------------------------------------------------
---
            male |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .0035275   .0145777     0.24   0.809    -.0250515    .0321
> 065
           _cons |   .4978474   .0098938    50.32   0.000     .4784509    .5172
> 438
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,729
-------------+----------------------------------   F(1, 4727)      =      0.08
       Model |  21.8975579         1  21.8975579   Prob > F        =    0.7748
    Residual |  1264961.97     4,727  267.603548   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  1264983.87     4,728   267.55158   Root MSE        =    16.359

-------------------------------------------------------------------------------
---
             age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |  -.1365115   .4772187    -0.29   0.775    -1.072082    .7990
> 594
           _cons |   39.55394   .3240119   122.08   0.000     38.91873    40.18
> 916
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     3,980
-------------+----------------------------------   F(1, 3978)      =      0.12
       Model |  3.01999183         1  3.01999183   Prob > F        =    0.7313
    Residual |  101866.873     3,978  25.6075599   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  101869.893     3,979  25.6018833   Root MSE        =    5.0604

-------------------------------------------------------------------------------
---
 householdincome |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |  -.0552827   .1609795    -0.34   0.731    -.3708928    .2603
> 273
           _cons |   7.379118   .1090085    67.69   0.000     7.165401    7.592
> 836
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,737
-------------+----------------------------------   F(1, 4735)      =    132.10
       Model |  295.370224         1  295.370224   Prob > F        =    0.0000
    Residual |  10587.4239     4,735  2.23599239   R-squared       =    0.0271
-------------+----------------------------------   Adj R-squared   =    0.0269
       Total |  10882.7942     4,736  2.29788728   Root MSE        =    1.4953

-------------------------------------------------------------------------------
---
        citysize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .5009706   .0435877    11.49   0.000     .4155184    .5864
> 228
           _cons |   3.139726   .0295828   106.13   0.000      3.08173    3.197
> 722
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,619
-------------+----------------------------------   F(1, 4617)      =      0.06
       Model |  1.21656208         1  1.21656208   Prob > F        =    0.8035
    Residual |  90744.6328     4,617   19.654458   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  90745.8493     4,618  19.6504654   Root MSE        =    4.4333

-------------------------------------------------------------------------------
---
       education |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .0325408    .130795     0.25   0.804    -.2238799    .2889
> 614
           _cons |   8.692401   .0891314    97.52   0.000     8.517661    8.867
> 141
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,737
-------------+----------------------------------   F(1, 4735)      =      0.00
       Model |   .00111196         1   .00111196   Prob > F        =    0.9463
    Residual |  1161.60666     4,735  .245323476   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  1161.60777     4,736  .245271911   Root MSE        =     .4953

-------------------------------------------------------------------------------
---
         working |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   -.000972   .0144377    -0.07   0.946    -.0292767    .0273
> 326
           _cons |   .4313112   .0097988    44.02   0.000     .4121009    .4505
> 214
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,672
-------------+----------------------------------   F(1, 4670)      =      0.18
       Model |  .038145854         1  .038145854   Prob > F        =    0.6675
    Residual |  965.322941     4,670  .206707268   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  965.361087     4,671  .206671181   Root MSE        =    .45465

-------------------------------------------------------------------------------
---
voted_lastpres~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .0057334   .0133465     0.43   0.668     -.020432    .0318
> 989
           _cons |    .705626   .0090497    77.97   0.000     .6878843    .7233
> 676
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,660
-------------+----------------------------------   F(1, 4658)      =      3.68
       Model |  .292484815         1  .292484815   Prob > F        =    0.0552
    Residual |  370.334983     4,658  .079505149   R-squared       =    0.0008
-------------+----------------------------------   Adj R-squared   =    0.0006
       Total |  370.627468     4,659  .079550862   Root MSE        =    .28197

-------------------------------------------------------------------------------
---
  voteregistered |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .0158918   .0082855     1.92   0.055    -.0003517    .0321
> 354
           _cons |   .9055401   .0056292   160.86   0.000     .8945041     .916
> 576
-------------------------------------------------------------------------------
---

      Source |       SS           df       MS      Number of obs   =     4,721
-------------+----------------------------------   F(1, 4719)      =      1.13
       Model |  .175031577         1  .175031577   Prob > F        =    0.2878
    Residual |   730.72266     4,719  .154846929   R-squared       =    0.0002
-------------+----------------------------------   Adj R-squared   =    0.0000
       Total |  730.897691     4,720  .154851206   Root MSE        =    .39351

-------------------------------------------------------------------------------
---
         remesas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interv
> al]
-----------------+-------------------------------------------------------------
---
posttrump_14days |   .0122152   .0114893     1.06   0.288    -.0103092    .0347
> 397
           _cons |   .1858546   .0078002    23.83   0.000     .1705625    .2011
> 467
-------------------------------------------------------------------------------
---

. 
. //Output the results of the 14 day window balance test
.         texdoc init balance_table_14days.tex, replace force
(texdoc output file is balance_table_14days.tex)

.         tex \begin{tabular}{lccc} \toprule \toprule

.         tex Variable                    &       Mean Control    & Mean Treatm
> ent & Difference \\

.         tex \addlinespace \hline \\

.         foreach var of varlist male age householdincome citysize education wo
> rking voted_lastpresidential voteregistered remesas{
  2.         tex ${lbe_`var'} & ${m`var'_0} & ${m`var'_1} & ${dif_`var'}${star_
> `var'}\\
  3.         }

.         tex \hline \hline

.         tex \end{tabular}

.         
. //Create balance table for 7 day window
. 
. foreach var of varlist male age householdincome citysize education working vo
> ted_lastpresidential voteregistered remesas{
  2.         
.         reg `var' posttrump_7days
  3.         global m`var'_0: di %6.3fc _b[_cons]
  4.         global m`var'_1: di %6.3fc _b[_cons] + _b[posttrump_7days]
  5.         global dif_`var': di %6.3fc _b[posttrump_7days]
  6. 
.         global lbe_`var' : var label `var'
  7. 
.         qui test posttrump_7days=0
  8.         global p_`var': di %12.3fc r(p)
  9.         glo star_`var'=cond(${p_`var'}<.001,"***",cond(${p_`var'}<.01,"**"
> ,cond(${p_`var'}<.05,"*",cond(${p_`var'}<.1,"+",""))))
 10. }

      Source |       SS           df       MS      Number of obs   =     2,659
-------------+----------------------------------   F(1, 2657)      =      0.14
       Model |  .034850839         1  .034850839   Prob > F        =    0.7090
    Residual |   664.69926     2,657  .250169085   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  664.734111     2,658  .250088078   Root MSE        =    .50017

-------------------------------------------------------------------------------
--
           male |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |   -.007242   .0194029    -0.37   0.709    -.0452884    .03080
> 44
          _cons |    .501107   .0135877    36.88   0.000     .4744634    .52775
> 06
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,654
-------------+----------------------------------   F(1, 2652)      =      0.04
       Model |  9.24811564         1  9.24811564   Prob > F        =    0.8515
    Residual |    699951.4     2,652  263.933409   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0004
       Total |  699960.648     2,653   263.83741   Root MSE        =    16.246

-------------------------------------------------------------------------------
--
            age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |  -.1180819   .6308176    -0.19   0.852    -1.355026    1.1188
> 62
          _cons |   39.52515   .4418337    89.46   0.000     38.65877    40.391
> 52
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,218
-------------+----------------------------------   F(1, 2216)      =      0.73
       Model |  18.4213004         1  18.4213004   Prob > F        =    0.3935
    Residual |  56034.0426     2,216  25.2861203   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0001
       Total |  56052.4639     2,217  25.2830239   Root MSE        =    5.0285

-------------------------------------------------------------------------------
--
householdincome |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |   .1822801   .2135601     0.85   0.393    -.2365188     .6010
> 79
          _cons |   6.876114   .1501221    45.80   0.000     6.581719    7.1705
> 09
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,659
-------------+----------------------------------   F(1, 2657)      =    133.21
       Model |  241.188228         1  241.188228   Prob > F        =    0.0000
    Residual |    4810.717     2,657  1.81058224   R-squared       =    0.0477
-------------+----------------------------------   Adj R-squared   =    0.0474
       Total |  5051.90523     2,658  1.90064155   Root MSE        =    1.3456

-------------------------------------------------------------------------------
--
       citysize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |   .6024608   .0521987    11.54   0.000     .5001065     .7048
> 15
          _cons |   3.247232   .0365544    88.83   0.000     3.175555     3.318
> 91
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,597
-------------+----------------------------------   F(1, 2595)      =      0.98
       Model |  18.9622971         1  18.9622971   Prob > F        =    0.3227
    Residual |  50300.1559     2,595  19.3834898   R-squared       =    0.0004
-------------+----------------------------------   Adj R-squared   =   -0.0000
       Total |  50319.1182     2,596  19.3833275   Root MSE        =    4.4027

-------------------------------------------------------------------------------
--
      education |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |   .1709145   .1728023     0.99   0.323    -.1679298    .50975
> 88
          _cons |   8.484043   .1213635    69.91   0.000     8.246063    8.7220
> 22
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,659
-------------+----------------------------------   F(1, 2657)      =      0.11
       Model |   .02584337         1   .02584337   Prob > F        =    0.7452
    Residual |  650.054638     2,657  .244657372   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  650.080481     2,658  .244575049   Root MSE        =    .49463

-------------------------------------------------------------------------------
--
        working |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |  -.0062363    .019188    -0.33   0.745    -.0438612    .03138
> 87
          _cons |   .4287823   .0134372    31.91   0.000     .4024338    .45513
> 08
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,618
-------------+----------------------------------   F(1, 2616)      =      1.81
       Model |  .365418485         1  .365418485   Prob > F        =    0.1790
    Residual |  529.159792     2,616  .202278208   R-squared       =    0.0007
-------------+----------------------------------   Adj R-squared   =    0.0003
       Total |   529.52521     2,617  .202340546   Root MSE        =    .44975

-------------------------------------------------------------------------------
--
voted_lastpre~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |    .023633   .0175832     1.34   0.179    -.0108454    .05811
> 15
          _cons |   .7068966   .0123139    57.41   0.000     .6827505    .73104
> 26
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,616
-------------+----------------------------------   F(1, 2614)      =      6.25
       Model |  .498338243         1  .498338243   Prob > F        =    0.0125
    Residual |  208.455408     2,614  .079745757   R-squared       =    0.0024
-------------+----------------------------------   Adj R-squared   =    0.0020
       Total |  208.953746     2,615   .07990583   Root MSE        =    .28239

-------------------------------------------------------------------------------
--
 voteregistered |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |   .0276104    .011045     2.50   0.012     .0059526    .04926
> 82
          _cons |   .8989521   .0077259   116.36   0.000     .8838025    .91410
> 16
-------------------------------------------------------------------------------
--

      Source |       SS           df       MS      Number of obs   =     2,650
-------------+----------------------------------   F(1, 2648)      =      3.31
       Model |  .490256948         1  .490256948   Prob > F        =    0.0689
    Residual |  391.928234     2,648  .148009152   R-squared       =    0.0012
-------------+----------------------------------   Adj R-squared   =    0.0009
       Total |  392.418491     2,649  .148138351   Root MSE        =    .38472

-------------------------------------------------------------------------------
--
        remesas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |    .027208   .0149496     1.82   0.069     -.002106     .0565
> 22
          _cons |   .1674074   .0104707    15.99   0.000     .1468757    .18793
> 91
-------------------------------------------------------------------------------
--

. 
. //Output the results of the 7 day window balance test
.         texdoc init balance_table_7days.tex, replace force
(texdoc output file is balance_table_7days.tex)

.         tex \begin{tabular}{lccc} \toprule \toprule

.         tex Variable                    &       Mean Control    & Mean Treatm
> ent & Difference \\

.         tex \addlinespace \hline \\

.         foreach var of varlist male age householdincome citysize education wo
> rking voted_lastpresidential voteregistered remesas{
  2.         tex ${lbe_`var'} & ${m`var'_0} & ${m`var'_1} & ${dif_`var'}${star_
> `var'}\\
  3.         }

.         tex \hline \hline

.         tex \end{tabular}

. 
. //NOTE: Three .tex files will now be saved in your working directory. Please 
> consult these files to see
. //the three separate tables that were compiled to create Appendix Table B.5. 
.         
.         
. *******************************
. *******************************
. //3. MAIN RESULTS IN FIGURE 2//
. *******************************
. *******************************
. 
. 
. // Effects of Trump's election on Trust in the US Government 
. 
. // We start with the full sample, then show +/- 7 days restricted bandwidth, 
> then restricted bandwidth plus covariates, 
. //then entropy balancing weights (but not covariates, given that we already b
> alanced on them). 
. //All models include country fixed effects and city size fixed effects. 
. 
. 
. eststo clear

. // Panel A: Full Sample with Country FE and City Size FE
. eststo m_1: reg trustusgov i.posttrump i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(8, 3976)      =     23.94
       Model |  156.273595         8  19.5341993   Prob > F        =    0.0000
    Residual |   3243.8579     3,976  .815859632   R-squared       =    0.0460
-------------+----------------------------------   Adj R-squared   =    0.0440
       Total |  3400.13149     3,984   .85344666   Root MSE        =    .90325

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
        1.posttrump |  -.3121887   .0318823    -9.79   0.000    -.3746959   -.2
> 496816
                    |
            country |
          Honduras  |   .1186274    .041225     2.88   0.004     .0378034    .1
> 994515
          Paraguay  |  -.0696897   .0426344    -1.63   0.102     -.153277    .0
> 138976
Dominican Republic  |   .2724112   .0401381     6.79   0.000      .193718    .3
> 511045
                    |
           citysize |
        Large City  |   .1264335   .0498504     2.54   0.011     .0286988    .2
> 241682
       Medium City  |   .0766808   .0448688     1.71   0.088    -.0112872    .1
> 646488
        Small City  |   .1179803   .0527984     2.23   0.026     .0144658    .2
> 214948
        Rural Area  |    .100977   .0406958     2.48   0.013     .0211904    .1
> 807636
                    |
              _cons |   2.862476   .0373795    76.58   0.000     2.789191    2.
> 935761
-------------------------------------------------------------------------------
------

. eststo m_2: reg trustusgov i.posttrump##c.time_zero i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(10, 3974)     =     19.80
       Model |  161.388995        10  16.1388995   Prob > F        =    0.0000
    Residual |   3238.7425     3,974  .814983014   R-squared       =    0.0475
-------------+----------------------------------   Adj R-squared   =    0.0451
       Total |  3400.13149     3,984   .85344666   Root MSE        =    .90276

-------------------------------------------------------------------------------
--------
           trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. I
> nterval]
----------------------+--------------------------------------------------------
--------
          1.posttrump |  -.2233299   .0529555    -4.22   0.000    -.3271523   -
> .1195074
            time_zero |  -.0082774   .0033761    -2.45   0.014    -.0148965   -
> .0016584
                      |
posttrump#c.time_zero |
                   1  |   .0062847   .0049672     1.27   0.206    -.0034538    
> .0160233
                      |
              country |
            Honduras  |   .0747127   .0449237     1.66   0.096    -.0133631    
> .1627884
            Paraguay  |  -.0766813   .0432422    -1.77   0.076    -.1614603    
> .0080977
  Dominican Republic  |   .2651262   .0403083     6.58   0.000     .1860993    
> .3441531
                      |
             citysize |
          Large City  |   .1555759   .0512416     3.04   0.002     .0551135    
> .2560382
         Medium City  |   .1107118   .0473086     2.34   0.019     .0179603    
> .2034633
          Small City  |   .1491423   .0545062     2.74   0.006     .0422795    
> .2560051
          Rural Area  |   .1332386   .0431543     3.09   0.002      .048632    
> .2178452
                      |
                _cons |   2.773484   .0535381    51.80   0.000      2.66852    
> 2.878449
-------------------------------------------------------------------------------
--------

. local n1 = `e(N)'

. 
. // Panel B: 7 day bandwidth with Country FE and City Size FE
. eststo m_3: reg trustusgov i.posttrump_7days i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(8, 1621)      =      8.23
       Model |  53.1253139         8  6.64066423   Prob > F        =    0.0000
    Residual |  1308.12377     1,621  .806985667   R-squared       =    0.0390
-------------+----------------------------------   Adj R-squared   =    0.0343
       Total |  1361.24908     1,629  .835634794   Root MSE        =    .89832

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.2235283    .047393    -4.72   0.000    -.3164863   -.1
> 305704
                    |
            country |
          Honduras  |   .0254886   .0761484     0.33   0.738    -.1238711    .1
> 748483
          Paraguay  |  -.0846369   .0747091    -1.13   0.257    -.2311735    .0
> 618997
Dominican Republic  |   .3319214   .0792543     4.19   0.000     .1764697    .4
> 873731
                    |
           citysize |
        Large City  |   .0136946   .1093602     0.13   0.900    -.2008077    .2
> 281969
       Medium City  |   .0731548   .0938077     0.78   0.436    -.1108423    .2
> 571519
        Small City  |   .0893339    .103221     0.87   0.387    -.1131266    .2
> 917945
        Rural Area  |   .0608135   .0907861     0.67   0.503     -.117257     .
> 238884
                    |
              _cons |   2.843559   .0642872    44.23   0.000     2.717465    2.
> 969654
-------------------------------------------------------------------------------
------

. eststo m_4: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e 

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(10, 1619)     =      7.00
       Model |  56.4253472        10  5.64253472   Prob > F        =    0.0000
    Residual |  1304.82373     1,619  .805944245   R-squared       =    0.0415
-------------+----------------------------------   Adj R-squared   =    0.0355
       Total |  1361.24908     1,629  .835634794   Root MSE        =    .89774

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1674952   .1002127    -1.67   0.095    -.3640554    .0
> 290651
          time_zero |   .0101702   .0172581     0.59   0.556    -.0236804    .0
> 440207
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0420598   .0237206    -1.77   0.076    -.0885861    .0
> 044666
                    |
            country |
          Honduras  |   .0378425   .0763515     0.50   0.620    -.1119156    .1
> 876006
          Paraguay  |  -.0806797   .0749194    -1.08   0.282    -.2276289    .0
> 662695
Dominican Republic  |   .3320068   .0796594     4.17   0.000     .1757604    .4
> 882532
                    |
           citysize |
        Large City  |   .0138933   .1093959     0.13   0.899    -.2006791    .2
> 284657
       Medium City  |   .0644569   .0938827     0.69   0.492    -.1196875    .2
> 486013
        Small City  |   .0849295   .1034464     0.82   0.412    -.1179734    .2
> 878323
        Rural Area  |   .0463807   .0910698     0.51   0.611    -.1322465    .2
> 250078
                    |
              _cons |   2.886566   .0936214    30.83   0.000     2.702934    3.
> 070198
-------------------------------------------------------------------------------
------

. local n3 = `e(N)'

. 
. // Panel C: 7 day bandwidth with Country FE and City Size FE plus covariate a
> djustment
. eststo m_5: reg trustusgov i.posttrump_7days i.country i.citysize male age ho
> useholdincome education working  voted_lastpresidential voteregistered remesa
> s 

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      4.67
       Model |  58.1562667        16  3.63476667   Prob > F        =    0.0000
    Residual |  1057.16906     1,357  .779048682   R-squared       =    0.0521
-------------+----------------------------------   Adj R-squared   =    0.0410
       Total |  1115.32533     1,373   .81232726   Root MSE        =    .88264

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.2439766   .0510181    -4.78   0.000    -.3440595   
> -.1438937
                       |
               country |
             Honduras  |   .1363954   .0811264     1.68   0.093    -.0227514   
>  .2955422
             Paraguay  |  -.0502324    .082858    -0.61   0.544    -.2127761   
>  .1123114
   Dominican Republic  |   .3846036    .084302     4.56   0.000     .2192272   
>    .54998
                       |
              citysize |
           Large City  |  -.0974061   .1162042    -0.84   0.402    -.3253655   
>  .1305533
          Medium City  |   .0381087   .0994869     0.38   0.702    -.1570562   
>  .2332736
           Small City  |   .0051433   .1105859     0.05   0.963    -.2117946   
>  .2220811
           Rural Area  |   .0975422   .0988062     0.99   0.324    -.0962873   
>  .2913717
                       |
                  male |   .1145764   .0506034     2.26   0.024     .0153069   
>  .2138458
                   age |   .0002108   .0017652     0.12   0.905     -.003252   
>  .0036737
       householdincome |   .0030952   .0057317     0.54   0.589    -.0081487   
>  .0143392
             education |   .0104757   .0068294     1.53   0.125    -.0029215   
>   .023873
               working |  -.0131936   .0527255    -0.25   0.802     -.116626   
>  .0902387
voted_lastpresidential |   .0856411   .0607633     1.41   0.159    -.0335591   
>  .2048414
        voteregistered |  -.1293084   .1038172    -1.25   0.213     -.332968   
>  .0743512
               remesas |    .053158   .0590689     0.90   0.368    -.0627183   
>  .1690344
                 _cons |   2.685848   .1553659    17.29   0.000     2.381065   
>  2.990632
-------------------------------------------------------------------------------
---------

. eststo m_6: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e male age householdincome education working  voted_lastpresidential voteregi
> stered remesas 

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(18, 1355)     =      4.32
       Model |    60.52872        18  3.36270666   Prob > F        =    0.0000
    Residual |  1054.79661     1,355  .778447681   R-squared       =    0.0543
-------------+----------------------------------   Adj R-squared   =    0.0417
       Total |  1115.32533     1,373   .81232726   Root MSE        =     .8823

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1647987   .1072764    -1.54   0.125    -.3752445   
>  .0456472
             time_zero |   .0037249   .0186987     0.20   0.842    -.0329566   
>  .0404064
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |  -.0344526   .0256107    -1.35   0.179    -.0846935   
>  .0157884
                       |
               country |
             Honduras  |   .1446693   .0812441     1.78   0.075    -.0147087   
>  .3040472
             Paraguay  |    -.04696   .0830093    -0.57   0.572    -.2098007   
>  .1158806
   Dominican Republic  |   .3815448   .0846666     4.51   0.000      .215453   
>  .5476366
                       |
              citysize |
           Large City  |  -.0938094   .1163201    -0.81   0.420    -.3219964   
>  .1343777
          Medium City  |   .0294597   .0995829     0.30   0.767    -.1658937   
>   .224813
           Small City  |  -.0000268   .1108741    -0.00   1.000    -.2175302   
>  .2174767
           Rural Area  |   .0846304   .0990584     0.85   0.393    -.1096942   
>   .278955
                       |
                  male |   .1118815    .050609     2.21   0.027      .012601   
>  .2111619
                   age |   .0003021   .0017658     0.17   0.864    -.0031618   
>  .0037661
       householdincome |   .0030029   .0057307     0.52   0.600    -.0082391   
>  .0142449
             education |   .0105829    .006827     1.55   0.121    -.0028097   
>  .0239755
               working |  -.0073261    .052813    -0.14   0.890    -.1109301   
>  .0962779
voted_lastpresidential |   .0856584   .0607399     1.41   0.159    -.0334961   
>  .2048129
        voteregistered |  -.1344301   .1038304    -1.29   0.196     -.338116   
>  .0692557
               remesas |   .0572788   .0592519     0.97   0.334    -.0589567   
>  .1735143
                 _cons |   2.702587   .1718492    15.73   0.000     2.365467   
>  3.039706
-------------------------------------------------------------------------------
---------

. local n5 = `e(N)'

. 
. // Panel D: 7 day bandwidth with Country FE, City Size FE, and entropy balanc
> ing weights
. eststo m_7: svy: reg trustusgov i.posttrump_7days i.country i.citysize
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(   8,   1367)   =       8.67
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0447

-------------------------------------------------------------------------------
------
                    |             Linearized
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.2335545    .050299    -4.64   0.000    -.3322256   -.1
> 348833
                    |
            country |
          Honduras  |   .1246594   .0814486     1.53   0.126    -.0351177    .2
> 844365
          Paraguay  |  -.0568783      .0786    -0.72   0.469    -.2110673    .0
> 973107
Dominican Republic  |   .3997802   .0829944     4.82   0.000     .2369708    .5
> 625897
                    |
           citysize |
        Large City  |  -.1202091   .1144848    -1.05   0.294     -.344793    .1
> 043747
       Medium City  |   .0132301   .0959849     0.14   0.890    -.1750627    .2
> 015229
        Small City  |  -.0237411   .1048816    -0.23   0.821    -.2294865    .1
> 820043
        Rural Area  |    .051135   .0899757     0.57   0.570    -.1253695    .2
> 276396
                    |
              _cons |   2.853524   .0634321    44.99   0.000      2.72909    2.
> 977959
-------------------------------------------------------------------------------
------

. eststo m_8: svy: reg trustusgov i.posttrump_7days##c.time_zero i.country i.ci
> tysize  
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(  10,   1365)   =       7.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0467

-------------------------------------------------------------------------------
------
                    |             Linearized
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1621407   .1051761    -1.54   0.123    -.3684639    .0
> 441825
          time_zero |   .0048307   .0184897     0.26   0.794    -.0314403    .0
> 411017
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0344097   .0255164    -1.35   0.178    -.0844651    .0
> 156457
                    |
            country |
          Honduras  |   .1332691   .0814193     1.64   0.102    -.0264504    .2
> 929886
          Paraguay  |  -.0536201   .0785185    -0.68   0.495    -.2076492     .
> 100409
Dominican Republic  |   .3974394     .08332     4.77   0.000     .2339912    .5
> 608875
                    |
           citysize |
        Large City  |  -.1162826   .1148026    -1.01   0.311    -.3414899    .1
> 089248
       Medium City  |   .0050115   .0960379     0.05   0.958    -.1833853    .1
> 934083
        Small City  |  -.0277957   .1050888    -0.26   0.791    -.2339476    .1
> 783562
        Rural Area  |   .0380223   .0901105     0.42   0.673    -.1387468    .2
> 147914
                    |
              _cons |   2.875315   .0939241    30.61   0.000     2.691065    3.
> 059565
-------------------------------------------------------------------------------
------

. local n7 = `e(N)'

. 
. // Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. // Generate Figure 2
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) || ///
>                         (m_7, msize(medsmall)) (m_8, msize(medsmall)), ///
>                         drop(*.country *.citysize male age householdincome  e
> ducation working  voted_lastpresidential voteregistered remesas _cons) xline(
> 0, lpattern(solid)) byopts(row(2)) levels(95 90)      ///
>                         bylabels("A. Full sample, N=`n1' " "B. ± 7 days, N=`n
> 3' " "C. ± 7 days & Covariates, N=`n5' " "D. ± 7 days & Balancing, N=`n7' ") 
> subtitle(, size(small)) nokey      ///
>                         rename(1.posttrump = 1.posttrump_7days ///
>                         1.posttrump_14days = 1.posttrump_7days ///
>                         1.posttrump_21days = 1.posttrump_7days ///
>                         1.posttrump#c.time_zero = 1.posttrump_7days#c.time_ze
> ro ///
>                         1.posttrump_14days#c.time_zero = 1.posttrump_7days#c.
> time_zero ///
>                         1.posttrump_21days#c.time_zero = 1.posttrump_7days#c.
> time_zero) ///
>                         coeflabel(1.posttrump_7days = "Treatment"       ///
>                         1.posttrump_7days#c.time_zero = "Treatment*Days" _con
> s = "Constant") ///
>                         aspect(.4) mlabgap(*2)   

. 
. addplot 1: , b1title("", size(small)) norescaling

. addplot 2: , b1title("") norescaling

. addplot 3: , b1title("Effect on Trust in US Gov't") norescaling

. addplot 4: , b1title("Effect on Trust in US Gov't") norescaling

. 
. graph save Figure_1.gph, replace 
(file Figure_1.gph saved)

. graph export Figure_1.png, replace 
(file Figure_1.png written in PNG format)

. 
. drop sample_reg 

. 
. *Generate accompanying table, which is Appendix Table B.2
. 
. esttab m* using main.tex, ///
>         drop(*.citysize *.country) unstack starlevels(+ .10 * 0.05 ** 0.01 **
> * 0.001) ///
>         cells(b(star fmt(%9.2f)) se(par)) stats(N, fmt(%9.0f %9.0g)) replace 
> label ///
>         mtitles("Full Sample" "\shortstack{Full Sample \\ with interaction}" 
> "7 day bandwidth" ///
>         "\shortstack{7 day bandwidth \\ with interaction}" "\shortstack{7 day
>  bandwidth \\ and covariates}" ///
>         "\shortstack{7 day bandwidth \\ and covariates \\ with interaction}" 
> ///
>         "\shortstack{7 day bandwidth \\ and entropy balancing}" "\shortstack{
> 7 day bandwidth \\ and entropy balancing \\ with interaction}") ///
>         nobaselevel collabels(, none) varlabels(_cons Constant) style(tex) pr
> ehead("\begin{tabular}{lcccccccc}" ///
>         \hline\hline) posthead(\hline) prefoot(\hline) postfoot(\hline\hline 
> ///
>         \multicolumn{8}{l}{\footnotesize $+ p<0.10 * p<0.05 ** p<0.01 *** p<0
> .001.$ ///
>         Standard errors in parentheses. All models include country and size o
> f location fixed effects.}\\ "\end{tabular}" )
(output written to main.tex)

.         
. ******************************************************
. ******************************************************
. //4. Predicted Probabilities by Country for Figure 3//
. ******************************************************
. ******************************************************
. 
. //Predicted probabilities, calculated separately for each country, are based 
> on are 7-day models (OLS) using covariates & size of location fixed effects
. 
. //Generate individual country graphs for Figure 3
. 
. //Dominican Republic
. eststo dr: reg dummytrustusg posttrump_7days elsalv paraguay honduras i.citys
> ize male age householdincome education working voted_lastpresidential votereg
> istered remesas

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      5.21
       Model |  17.4147656        16  1.08842285   Prob > F        =    0.0000
    Residual |  283.462236     1,357  .208888899   R-squared       =    0.0579
-------------+----------------------------------   Adj R-squared   =    0.0468
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45704

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1433926    .026418    -5.43   0.000    -.1952171   
> -.0915681
                elsalv |  -.2144425   .0436529    -4.91   0.000     -.300077   
> -.1288079
              paraguay |  -.2161637    .037967    -5.69   0.000    -.2906441   
> -.1416834
              honduras |  -.1467083   .0378266    -3.88   0.000    -.2209133   
> -.0725033
                       |
              citysize |
           Large City  |  -.0764163   .0601724    -1.27   0.204    -.1944572   
>  .0416247
          Medium City  |   .0181522   .0515159     0.35   0.725    -.0829072   
>  .1192117
           Small City  |   .0203409   .0572631     0.36   0.722     -.091993   
>  .1326748
           Rural Area  |   .0591371   .0511634     1.16   0.248    -.0412309   
>  .1595051
                       |
                  male |   .0079392   .0262033     0.30   0.762    -.0434641   
>  .0593425
                   age |  -.0002035   .0009141    -0.22   0.824    -.0019966   
>  .0015896
       householdincome |  -.0014523    .002968    -0.49   0.625    -.0072746   
>    .00437
             education |   .0115795   .0035364     3.27   0.001     .0046422   
>  .0185168
               working |  -.0001602   .0273021    -0.01   0.995     -.053719   
>  .0533987
voted_lastpresidential |   .0186391   .0314642     0.59   0.554    -.0430847   
>  .0803629
        voteregistered |  -.0674139   .0537582    -1.25   0.210    -.1728721   
>  .0380442
               remesas |   .0424968   .0305868     1.39   0.165    -.0175058   
>  .1024994
                 _cons |   .8193863   .0909545     9.01   0.000     .6409597   
>  .9978129
-------------------------------------------------------------------------------
---------

. margins, at(posttrump_7days = (0 1) elsalv = 0 paraguay = 0 honduras = 0)

Predictive margins                              Number of obs     =      1,374
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : postt~_7days    =           0
               elsalv          =           0
               paraguay        =           0
               honduras        =           0

2._at        : postt~_7days    =           1
               elsalv          =           0
               paraguay        =           0
               honduras        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .8906966   .0310985    28.64   0.000     .8296903    .9517029
          2  |   .7473039   .0290913    25.69   0.000     .6902351    .8043728
------------------------------------------------------------------------------

. marginsplot, recast(scatter) xscale(range(-.5 1.5)) title("Dominican Republic
> ") ytitle("") xlabel(0 "Before Election" 1 "After Election", labsize(vsmall))
>  xtitle("") aspect(2.5)

  Variables that uniquely identify margins: posttrump_7days

. 
. graph save predictedprob_DR.gph, replace 
(file predictedprob_DR.gph saved)

. graph export predictedprob_DR.png, replace 
(file predictedprob_DR.png written in PNG format)

. 
. //El Salvador
. eststo es: reg dummytrustusg posttrump_7days elsalv paraguay honduras i.citys
> ize male age householdincome education working voted_lastpresidential votereg
> istered remesas

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      5.21
       Model |  17.4147656        16  1.08842285   Prob > F        =    0.0000
    Residual |  283.462236     1,357  .208888899   R-squared       =    0.0579
-------------+----------------------------------   Adj R-squared   =    0.0468
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45704

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1433926    .026418    -5.43   0.000    -.1952171   
> -.0915681
                elsalv |  -.2144425   .0436529    -4.91   0.000     -.300077   
> -.1288079
              paraguay |  -.2161637    .037967    -5.69   0.000    -.2906441   
> -.1416834
              honduras |  -.1467083   .0378266    -3.88   0.000    -.2209133   
> -.0725033
                       |
              citysize |
           Large City  |  -.0764163   .0601724    -1.27   0.204    -.1944572   
>  .0416247
          Medium City  |   .0181522   .0515159     0.35   0.725    -.0829072   
>  .1192117
           Small City  |   .0203409   .0572631     0.36   0.722     -.091993   
>  .1326748
           Rural Area  |   .0591371   .0511634     1.16   0.248    -.0412309   
>  .1595051
                       |
                  male |   .0079392   .0262033     0.30   0.762    -.0434641   
>  .0593425
                   age |  -.0002035   .0009141    -0.22   0.824    -.0019966   
>  .0015896
       householdincome |  -.0014523    .002968    -0.49   0.625    -.0072746   
>    .00437
             education |   .0115795   .0035364     3.27   0.001     .0046422   
>  .0185168
               working |  -.0001602   .0273021    -0.01   0.995     -.053719   
>  .0533987
voted_lastpresidential |   .0186391   .0314642     0.59   0.554    -.0430847   
>  .0803629
        voteregistered |  -.0674139   .0537582    -1.25   0.210    -.1728721   
>  .0380442
               remesas |   .0424968   .0305868     1.39   0.165    -.0175058   
>  .1024994
                 _cons |   .8193863   .0909545     9.01   0.000     .6409597   
>  .9978129
-------------------------------------------------------------------------------
---------

. margins, at(posttrump_7days = (0 1) elsalv = 1 paraguay = 0 honduras = 0)

Predictive margins                              Number of obs     =      1,374
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : postt~_7days    =           0
               elsalv          =           1
               paraguay        =           0
               honduras        =           0

2._at        : postt~_7days    =           1
               elsalv          =           1
               paraguay        =           0
               honduras        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6762541   .0335661    20.15   0.000     .6104071    .7421011
          2  |   .5328615   .0314935    16.92   0.000     .4710802    .5946427
------------------------------------------------------------------------------

. marginsplot, recast(scatter) xscale(range(-.5 1.5)) title("El Salvador") ytit
> le("") xlabel(0 "Before Election" 1 "After Election", labsize(vsmall))  xtitl
> e("") aspect(2.5)

  Variables that uniquely identify margins: posttrump_7days

. 
. graph save predictedprob_ES.gph, replace 
(file predictedprob_ES.gph saved)

. graph export predictedprob_ES.png, replace 
(file predictedprob_ES.png written in PNG format)

. 
. //Honduras
. eststo hon: reg dummytrustusg posttrump_7days elsalv paraguay honduras i.city
> size male age householdincome education working voted_lastpresidential votere
> gistered remesas

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      5.21
       Model |  17.4147656        16  1.08842285   Prob > F        =    0.0000
    Residual |  283.462236     1,357  .208888899   R-squared       =    0.0579
-------------+----------------------------------   Adj R-squared   =    0.0468
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45704

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1433926    .026418    -5.43   0.000    -.1952171   
> -.0915681
                elsalv |  -.2144425   .0436529    -4.91   0.000     -.300077   
> -.1288079
              paraguay |  -.2161637    .037967    -5.69   0.000    -.2906441   
> -.1416834
              honduras |  -.1467083   .0378266    -3.88   0.000    -.2209133   
> -.0725033
                       |
              citysize |
           Large City  |  -.0764163   .0601724    -1.27   0.204    -.1944572   
>  .0416247
          Medium City  |   .0181522   .0515159     0.35   0.725    -.0829072   
>  .1192117
           Small City  |   .0203409   .0572631     0.36   0.722     -.091993   
>  .1326748
           Rural Area  |   .0591371   .0511634     1.16   0.248    -.0412309   
>  .1595051
                       |
                  male |   .0079392   .0262033     0.30   0.762    -.0434641   
>  .0593425
                   age |  -.0002035   .0009141    -0.22   0.824    -.0019966   
>  .0015896
       householdincome |  -.0014523    .002968    -0.49   0.625    -.0072746   
>    .00437
             education |   .0115795   .0035364     3.27   0.001     .0046422   
>  .0185168
               working |  -.0001602   .0273021    -0.01   0.995     -.053719   
>  .0533987
voted_lastpresidential |   .0186391   .0314642     0.59   0.554    -.0430847   
>  .0803629
        voteregistered |  -.0674139   .0537582    -1.25   0.210    -.1728721   
>  .0380442
               remesas |   .0424968   .0305868     1.39   0.165    -.0175058   
>  .1024994
                 _cons |   .8193863   .0909545     9.01   0.000     .6409597   
>  .9978129
-------------------------------------------------------------------------------
---------

. margins, at(posttrump_7days = (0 1) elsalv = 0 paraguay = 0 honduras = 1)

Predictive margins                              Number of obs     =      1,374
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : postt~_7days    =           0
               elsalv          =           0
               paraguay        =           0
               honduras        =           1

2._at        : postt~_7days    =           1
               elsalv          =           0
               paraguay        =           0
               honduras        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .7439883   .0286696    25.95   0.000     .6877467    .8002299
          2  |   .6005956   .0307009    19.56   0.000     .5403693     .660822
------------------------------------------------------------------------------

. marginsplot, recast(scatter) xscale(range(-.5 1.5)) title("Honduras") ytitle(
> "") xlabel(0 "Before Election" 1 "After Election", labsize(vsmall))  xtitle("
> ") aspect(2.5)

  Variables that uniquely identify margins: posttrump_7days

. 
. graph save predictedprob_HON.gph, replace 
(file predictedprob_HON.gph saved)

. graph export predictedprob_HON.png, replace 
(file predictedprob_HON.png written in PNG format)

. 
. //Paraguay
. eststo py: reg dummytrustusg posttrump_7days elsalv paraguay honduras i.citys
> ize male age householdincome education working voted_lastpresidential votereg
> istered remesas

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      5.21
       Model |  17.4147656        16  1.08842285   Prob > F        =    0.0000
    Residual |  283.462236     1,357  .208888899   R-squared       =    0.0579
-------------+----------------------------------   Adj R-squared   =    0.0468
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45704

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1433926    .026418    -5.43   0.000    -.1952171   
> -.0915681
                elsalv |  -.2144425   .0436529    -4.91   0.000     -.300077   
> -.1288079
              paraguay |  -.2161637    .037967    -5.69   0.000    -.2906441   
> -.1416834
              honduras |  -.1467083   .0378266    -3.88   0.000    -.2209133   
> -.0725033
                       |
              citysize |
           Large City  |  -.0764163   .0601724    -1.27   0.204    -.1944572   
>  .0416247
          Medium City  |   .0181522   .0515159     0.35   0.725    -.0829072   
>  .1192117
           Small City  |   .0203409   .0572631     0.36   0.722     -.091993   
>  .1326748
           Rural Area  |   .0591371   .0511634     1.16   0.248    -.0412309   
>  .1595051
                       |
                  male |   .0079392   .0262033     0.30   0.762    -.0434641   
>  .0593425
                   age |  -.0002035   .0009141    -0.22   0.824    -.0019966   
>  .0015896
       householdincome |  -.0014523    .002968    -0.49   0.625    -.0072746   
>    .00437
             education |   .0115795   .0035364     3.27   0.001     .0046422   
>  .0185168
               working |  -.0001602   .0273021    -0.01   0.995     -.053719   
>  .0533987
voted_lastpresidential |   .0186391   .0314642     0.59   0.554    -.0430847   
>  .0803629
        voteregistered |  -.0674139   .0537582    -1.25   0.210    -.1728721   
>  .0380442
               remesas |   .0424968   .0305868     1.39   0.165    -.0175058   
>  .1024994
                 _cons |   .8193863   .0909545     9.01   0.000     .6409597   
>  .9978129
-------------------------------------------------------------------------------
---------

. margins, at(posttrump_7days = (0 1) elsalv = 0 paraguay = 1 honduras = 0)

Predictive margins                              Number of obs     =      1,374
Model VCE    : OLS

Expression   : Linear prediction, predict()

1._at        : postt~_7days    =           0
               elsalv          =           0
               paraguay        =           1
               honduras        =           0

2._at        : postt~_7days    =           1
               elsalv          =           0
               paraguay        =           1
               honduras        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .6745329   .0284194    23.73   0.000     .6187821    .7302837
          2  |   .5311402   .0318516    16.68   0.000     .4686566    .5936239
------------------------------------------------------------------------------

. marginsplot, recast(scatter) xscale(range(-.5 1.5)) title("Paraguay") ytitle(
> "") xlabel(0 "Before Election" 1 "After Election", labsize(vsmall))  xtitle("
> ") aspect(2.5)

  Variables that uniquely identify margins: posttrump_7days

. 
. graph save predictedprob_PAR.gph, replace 
(file predictedprob_PAR.gph saved)

. graph export predictedprob_PAR.png, replace 
(file predictedprob_PAR.png written in PNG format)

. 
. //Combine individual country graphs for combined Figure 3
. gr combine predictedprob_DR.gph predictedprob_ES.gph predictedprob_HON.gph pr
> edictedprob_PAR.gph, ycommon row(1)  l1("Predicted Probability") 

. 
. graph save predictedprob_combined.gph, replace
(file predictedprob_combined.gph saved)

. graph export predictedprob_combined.png, replace
(file predictedprob_combined.png written in PNG format)

. 
. *Output results for Figure 3 in table form
. 
. esttab dr using eachcountry.tex, ///
>         drop(*.citysize ) unstack starlevels(+ .10 * 0.05 ** 0.01 *** 0.001) 
> ///
>         cells(b(star fmt(%9.2f)) se(par)) stats(N, fmt(%9.0f %9.0g)) replace 
> label ///
>         mtitles("Trust in the US Government")  ///
>         nobaselevel collabels(, none) varlabels(_cons Constant posttrump_7day
> s "Post Trump, 7-day Window" citysize "City Size" male "Male" age "Age" ///
>         householdincome "Household Income"  education "Education" working "Wo
> rking" votedlast "Voted Last Presidential Election" ///
>         voteregistered "Registered to Vote" remesas "Remittances" elsalv "El 
> Salvador" paraguay "Paraguay" honduras "Honduras") style(tex) prehead("\begin
> {tabular}{lc}" ///
>         \hline\hline) posthead(\hline) prefoot(\hline) postfoot(\hline\hline 
> ///
>         \multicolumn{2}{l}{\footnotesize $+ p<0.10 * p<0.05 ** p<0.01 *** p<0
> .001.$ ///
>         Standard errors in parentheses. \\ All models include size of locatio
> n fixed effects.}\\ "\end{tabular}" )
(output written to eachcountry.tex)

.         
. ****************************************
. ****************************************
. //5. Placebo tests for Figures 4 and 5//
. ****************************************
. ****************************************
. 
. **Placebo test 1 -- Faux election & FIGURE 4**
. 
. //Muñoz et al recommend a placebo test with a fictional 'treatment'
. //First, we find the median date the control subjects were interviewed and th
> en designate
. //that date as the "treatment." Then, using only control subjects,
. //we provide a placebo test for pre/post treatment differences
. 
. //Because fieldwork began on slightly different dates in each of our 4 countr
> ies, the
. //median date is different across countries.
. 
. //These models use covariates, 7-day bandwidth, and country and size of locat
> ion fixed effects.//
. //As such, they are comparable to models reported in Panel C of Figure 2.//
. 
. 
. //Create placebo variable by country
. sum fecha if posttrump==0 & country==3, detail

                      Date of Interview
-------------------------------------------------------------
      Percentiles      Smallest
 1%        20753          20753
 5%        20753          20753
10%        20754          20753       Obs                 614
25%        20756          20753       Sum of Wgt.         614

50%        20759                      Mean           20759.04
                        Largest       Std. Dev.      3.897575
75%        20763          20765
90%        20764          20765       Variance       15.19109
95%        20765          20765       Skewness       .0353091
99%        20765          20765       Kurtosis       1.656311

. 
. //Among pre-treatment observations in El Salvador, the median interview data 
> is 20759.//
. //We call the median, "Placebo Election Day." That means our first post-treat
> ment date is the median+1//
. gen placebo=1 if fecha>20759 & fecha<=20766 & country==3
(5,892 missing values generated)

. replace placebo=0 if fecha<=20759 & country==3
(349 real changes made)

. 
. sum fecha if posttrump==0 & country==4, detail

                      Date of Interview
-------------------------------------------------------------
      Percentiles      Smallest
 1%        20741          20741
 5%        20742          20741
10%        20744          20741       Obs               1,193
25%        20748          20741       Sum of Wgt.       1,193

50%        20754                      Mean           20753.89
                        Largest       Std. Dev.      6.988782
75%        20760          20766
90%        20763          20766       Variance       48.84308
95%        20765          20766       Skewness      -.0706079
99%        20766          20766       Kurtosis       1.961725

. 
. //Among pre-treatment observations in Honduras, the median interview data is 
> 20754.//
. replace placebo=1 if fecha>20754 & fecha<=20766 & country==4
(575 real changes made)

. replace placebo=0 if fecha<=20754 & country==4
(618 real changes made)

. 
. sum fecha if posttrump==0 & country==12, detail

                      Date of Interview
-------------------------------------------------------------
      Percentiles      Smallest
 1%        20746          20746
 5%        20748          20746
10%        20751          20746       Obs                 910
25%        20756          20746       Sum of Wgt.         910

50%        20762                      Mean            20759.5
                        Largest       Std. Dev.      5.340495
75%        20764          20766
90%        20765          20766       Variance       28.52088
95%        20766          20766       Skewness      -.8364296
99%        20766          20766       Kurtosis       2.784687

. 
. //Among pre-treatment observations in Paraguay, the median interview data is 
> 20762.//
. replace placebo=1 if fecha>20762 & fecha<=20766 & country==12
(351 real changes made)

. replace placebo=0 if fecha<=20762 & country==12
(559 real changes made)

. 
. sum fecha if posttrump==0 & country==21, detail

                      Date of Interview
-------------------------------------------------------------
      Percentiles      Smallest
 1%        20749          20749
 5%        20749          20749
10%        20750          20749       Obs                 641
25%        20752          20749       Sum of Wgt.         641

50%        20756                      Mean           20757.06
                        Largest       Std. Dev.      5.197317
75%        20761          20766
90%        20763          20766       Variance       27.01211
95%        20766          20766       Skewness       .0481767
99%        20766          20766       Kurtosis       1.725375

. 
. //Among pre-treatment observations in the Dominican Republic, the median inte
> rview data is 20756.//
. replace placebo=1 if fecha>20756 & fecha<=20766 & country==21
(314 real changes made)

. replace placebo=0 if fecha<=20756 & country==21
(327 real changes made)

. 
. //Create running time variable by country. We create a new time_zero variable
> , where zero is the median+1 date in each country
. 
. //Code the placebo time zero variable for El Salvador
. gen placebo_time_zero=.
(6,157 missing values generated)

. replace placebo_time_zero=fecha-20760 if country==3
(1,551 real changes made)

. 
. //Code the placebo time zero variable for Honduras
. replace placebo_time_zero=fecha-20755 if country==4
(1,560 real changes made)

. 
. //Code the placebo time zero variable for Paraguay
. replace placebo_time_zero=fecha-20763 if country==12
(1,528 real changes made)

. 
. //Code the placebo time zero variable for the Dominican Republic
. replace placebo_time_zero=fecha-20757 if country==21
(1,518 real changes made)

. 
. //Generate a 7-day placebo bandwidth
. gen placebo_7days=.
(6,157 missing values generated)

. replace placebo_7days=0 if placebo==0 & placebo_time_zero<=6 & placebo_time_z
> ero>=-7 
(1,378 real changes made)

. replace placebo_7days=1 if placebo==1 & placebo_time_zero<=6 & placebo_time_z
> ero>=-7 
(1,249 real changes made)

. label var placebo_time_zero "Days"

. 
. //Run the placebo test. All models include a running time variable interactio
> n, as with our main models.
.  
. eststo clear

. 
. //Fake Election Placebo Test, Naive  
. 
. eststo m_1: reg trustusgov i.placebo_7days

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(1, 1713)      =      0.44
       Model |  .331149263         1  .331149263   Prob > F        =    0.5073
    Residual |  1289.78197     1,713  .752937519   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0003
       Total |  1290.11312     1,714  .752691435   Root MSE        =    .86772

-------------------------------------------------------------------------------
--
     trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
1.placebo_7days |  -.0278885   .0420526    -0.66   0.507    -.1103683    .05459
> 13
          _cons |   3.035522    .028469   106.63   0.000     2.979684     3.091
> 36
-------------------------------------------------------------------------------
--

. eststo m_2: reg trustusgov i.placebo_7days##c.placebo_time_zero

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(3, 1711)      =      1.34
       Model |  3.02471094         3  1.00823698   Prob > F        =    0.2596
    Residual |  1287.08841     1,711  .752243371   R-squared       =    0.0023
-------------+----------------------------------   Adj R-squared   =    0.0006
       Total |  1290.11312     1,714  .752691435   Root MSE        =    .86732

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0571229   .0865511    -0.66   0.509      -.22688    .1
> 126341
  placebo_time_zero |  -.0129352   .0137716    -0.94   0.348    -.0399461    .0
> 140757
                    |
      placebo_7days#|
c.placebo_time_zero |
                 1  |   .0412998   .0220863     1.87   0.062    -.0020192    .0
> 846189
                    |
              _cons |   2.982333   .0633755    47.06   0.000     2.858031    3.
> 106635
-------------------------------------------------------------------------------
------

. 
. //Fake Election Placebo Test, Fixed Effects Only
. 
. eststo m_3: reg trustusgov i.placebo_7days i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(8, 1706)      =      5.44
       Model |  32.0737784         8   4.0092223   Prob > F        =    0.0000
    Residual |  1258.03934     1,706  .737420481   R-squared       =    0.0249
-------------+----------------------------------   Adj R-squared   =    0.0203
       Total |  1290.11312     1,714  .752691435   Root MSE        =    .85873

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0573232   .0429844    -1.33   0.183     -.141631    .0
> 269845
                    |
            country |
          Honduras  |    .168185   .0642857     2.62   0.009     .0420979    .2
> 942721
          Paraguay  |   -.085379   .0665408    -1.28   0.200    -.2158891    .0
> 451311
Dominican Republic  |     .23393   .0602213     3.88   0.000     .1158145    .3
> 520454
                    |
           citysize |
        Large City  |   .1501355      .0718     2.09   0.037     .0093101    .2
> 909608
       Medium City  |   .0908226   .0730815     1.24   0.214    -.0525163    .2
> 341614
        Small City  |   .1346574   .0794706     1.69   0.090    -.0212126    .2
> 905275
        Rural Area  |    .142231   .0692463     2.05   0.040     .0064144    .2
> 780476
                    |
              _cons |   2.875241   .0487524    58.98   0.000      2.77962    2.
> 970862
-------------------------------------------------------------------------------
------

. eststo m_4: reg trustusgov i.placebo_7days##c.placebo_time_zero i.country i.c
> itysize

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(10, 1704)     =      4.36
       Model |  32.1600877        10  3.21600877   Prob > F        =    0.0000
    Residual |  1257.95303     1,704  .738235347   R-squared       =    0.0249
-------------+----------------------------------   Adj R-squared   =    0.0192
       Total |  1290.11312     1,714  .752691435   Root MSE        =    .85921

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0397818   .0891136    -0.45   0.655    -.2145655    .1
> 350018
  placebo_time_zero |  -.0047523   .0139634    -0.34   0.734    -.0321395    .0
> 226349
                    |
      placebo_7days#|
c.placebo_time_zero |
                 1  |   .0055665   .0236356     0.24   0.814    -.0407914    .0
> 519244
                    |
            country |
          Honduras  |   .1687596   .0644727     2.62   0.009     .0423057    .2
> 952135
          Paraguay  |  -.0840099   .0692536    -1.21   0.225     -.219841    .0
> 518212
Dominican Republic  |   .2332059   .0607223     3.84   0.000     .1141078     .
> 352304
                    |
           citysize |
        Large City  |   .1446615   .0743691     1.95   0.052     -.001203    .2
> 905259
       Medium City  |   .0887579   .0740084     1.20   0.231     -.056399    .2
> 339148
        Small City  |   .1321306   .0798573     1.65   0.098     -.024498    .2
> 887592
        Rural Area  |   .1402267   .0696244     2.01   0.044     .0036683    .2
> 767852
                    |
              _cons |   2.857295   .0719019    39.74   0.000      2.71627     2
> .99832
-------------------------------------------------------------------------------
------

. 
. //Fake Election Placebo Test, Fixed Effects Plus Covariate Adjustment
. 
. eststo m_5: reg trustusgov i.placebo_7days i.country i.citysize male age educ
> ation working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,424
-------------+----------------------------------   F(16, 1407)     =      3.06
       Model |  35.8795813        16  2.24247383   Prob > F        =    0.0000
    Residual |  1030.86691     1,407  .732670155   R-squared       =    0.0336
-------------+----------------------------------   Adj R-squared   =    0.0226
       Total |  1066.74649     1,423  .749646162   Root MSE        =    .85596

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       1.placebo_7days |  -.0376152   .0470442    -0.80   0.424    -.1298996   
>  .0546691
                       |
               country |
             Honduras  |   .2119639    .069614     3.04   0.002     .0754055   
>  .3485222
             Paraguay  |  -.0673562   .0742634    -0.91   0.365    -.2130351   
>  .0783226
   Dominican Republic  |   .2141626   .0655526     3.27   0.001     .0855712   
>   .342754
                       |
              citysize |
           Large City  |   .1340005   .0785317     1.71   0.088    -.0200512   
>  .2880523
          Medium City  |    .127268   .0809557     1.57   0.116    -.0315389   
>  .2860749
           Small City  |   .1159111   .0878019     1.32   0.187    -.0563256   
>  .2881478
           Rural Area  |   .1902272   .0806267     2.36   0.018     .0320658   
>  .3483886
                       |
                  male |   .0585931   .0478742     1.22   0.221    -.0353194   
>  .1525057
                   age |   -.002112   .0016292    -1.30   0.195     -.005308   
>   .001084
             education |   .0044383   .0065042     0.68   0.495    -.0083206   
>  .0171972
               working |  -.0452197   .0501552    -0.90   0.367    -.1436067   
>  .0531673
voted_lastpresidential |   .1039161   .0575977     1.80   0.071    -.0090705   
>  .2169027
       householdincome |   .0051755   .0053075     0.98   0.330    -.0052361   
>   .015587
        voteregistered |  -.0491383   .0974082    -0.50   0.614    -.2402193   
>  .1419426
               remesas |   .0359496   .0559958     0.64   0.521    -.0738946   
>  .1457939
                 _cons |   2.779959   .1457221    19.08   0.000     2.494103   
>  3.065815
-------------------------------------------------------------------------------
---------

. eststo m_6: reg trustusgov i.placebo_7days##c.placebo_time_zero i.country i.c
> itysize male age education working voted_last household voteregistered remesa
> s

      Source |       SS           df       MS      Number of obs   =     1,424
-------------+----------------------------------   F(18, 1405)     =      2.79
       Model |   36.755021        18  2.04194561   Prob > F        =    0.0001
    Residual |  1029.99147     1,405  .733090013   R-squared       =    0.0345
-------------+----------------------------------   Adj R-squared   =    0.0221
       Total |  1066.74649     1,423  .749646162   Root MSE        =    .85621

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       1.placebo_7days |   .0000665   .0983116     0.00   0.999    -.1927868   
>  .1929198
     placebo_time_zero |  -.0154302   .0152649    -1.01   0.312    -.0453746   
>  .0145142
                       |
         placebo_7days#|
   c.placebo_time_zero |
                    1  |   .0247953   .0261086     0.95   0.342    -.0264208   
>  .0760114
                       |
               country |
             Honduras  |   .2150475   .0698201     3.08   0.002     .0780845   
>  .3520104
             Paraguay  |   -.054713   .0774518    -0.71   0.480    -.2066465   
>  .0972205
   Dominican Republic  |   .2083221   .0662561     3.14   0.002     .0783506   
>  .3382936
                       |
              citysize |
           Large City  |   .1103564   .0817932     1.35   0.177    -.0500935   
>  .2708063
          Medium City  |   .1149625   .0821888     1.40   0.162    -.0462635   
>  .2761885
           Small City  |   .1065741   .0882656     1.21   0.227    -.0665724   
>  .2797206
           Rural Area  |   .1806308   .0811814     2.23   0.026      .021381   
>  .3398805
                       |
                  male |   .0589712   .0478931     1.23   0.218    -.0349786   
>  .1529209
                   age |  -.0021176   .0016298    -1.30   0.194    -.0053147   
>  .0010795
             education |   .0046467   .0065105     0.71   0.476    -.0081245   
>   .017418
               working |  -.0455549   .0501824    -0.91   0.364    -.1439953   
>  .0528855
voted_lastpresidential |     .10402   .0576684     1.80   0.071    -.0091054   
>  .2171454
       householdincome |   .0049537   .0053156     0.93   0.352    -.0054736   
>  .0153811
        voteregistered |   -.047505   .0974488    -0.49   0.626    -.2386657   
>  .1436558
               remesas |    .036962   .0560309     0.66   0.510    -.0729512   
>  .1468753
                 _cons |   2.720977   .1576242    17.26   0.000     2.411773   
>  3.030181
-------------------------------------------------------------------------------
---------

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure 4
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) ,  ///
>                         drop(*.country *.citysize male age householdincome ci
> tysize education working  voted_lastpresidential voteregistered remesas _cons
> ) xline(0, lpattern(solid)) byopts(row(1)) levels(95 90)      ///
>                         bylabels("A. ± 7 days, Naive" "B. ± 7 days, FE" "C. ±
>  7 days, FE & Covariates") subtitle(, size(small)) nokey        ///
>                         coeflabel(1.placebo_7days = "Placebo Election"  ///
>                         1.placebo_7days#c.placebo_time_zero = "Placebo Electi
> on*Days" _cons = "Constant") ///
>                         aspect(.4) mlabgap(*2) 

. 
. addplot 1: , b1title("Effect on Trust in US Gov't", size(small)) norescaling

. addplot 2: , b1title("Effect on Trust in US Gov't") norescaling

. addplot 3: , b1title("Effect on Trust in US Gov't") norescaling

. 
. graph save Figure_placebo2.gph, replace 
(file Figure_placebo2.gph saved)

. graph export Figure_placebo2.png, replace 
(file Figure_placebo2.png written in PNG format)

. 
. *Generate accompanying table, which is Appendix Table B.6.
. 
. esttab m* using placebo_fakeelection.tex, ///
>         drop(*.citysize *.country) unstack starlevels(+ .10 * 0.05 ** 0.01 **
> * 0.001) ///
>         cells(b(star fmt(%9.2f)) se(par)) stats(N, fmt(%9.0f %9.0g)) replace 
> label ///
>         mtitles("± 7 Days" "\shortstack{± 7 Days \\ with interaction}" "± 7 D
> ays, Fixed Effects" ///
>         "\shortstack{± 7 Days, Fixed Effects \\ with interaction}" "\shortsta
> ck{± 7 Days,\\ Cov. Adj.}" ///
>         "\shortstack{± 7 Days,\\ Cov. Adj. \\ with interaction}") ///
>         nobaselevel collabels(, none) varlabels(_cons Constant) style(tex) pr
> ehead("\begin{tabular}{lcccccc}" ///
>         \hline\hline) posthead(\hline) prefoot(\hline) postfoot(\hline\hline 
> ///
>         \multicolumn{7}{l}{\footnotesize $+ p<0.10 * p<0.05 ** p<0.01 *** p<0
> .001.$ ///
>         Standard errors in parentheses. All models include country and size o
> f location fixed effects.}\\ "\end{tabular}" )
(output written to placebo_fakeelection.tex)

.  
. drop sample_reg

. 
. 
. ** Placebo test 2: trust in other foreign entities for Figure 5 **
. 
. //These models use covariates, 7-day bandwidth, and country and size of locat
> ion fixed effects.//
. //As such, they are comparable to models reported in Panel C of Figure 2.//
. 
. eststo clear

. 
. //Trust in China
. eststo m_1: reg trustchina i.posttrump_7days i.country i.citysize male age ed
> ucation working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =       766
-------------+----------------------------------   F(16, 749)      =      3.53
       Model |  53.1515079        16  3.32196924   Prob > F        =    0.0000
    Residual |  704.169641       749  .940146383   R-squared       =    0.0702
-------------+----------------------------------   Adj R-squared   =    0.0503
       Total |  757.321149       765  .989962286   Root MSE        =    .96961

-------------------------------------------------------------------------------
---------
            trustchina |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0168235   .0769932     0.22   0.827    -.1343247   
>  .1679717
                       |
               country |
             Honduras  |  -.0399581   .1249092    -0.32   0.749     -.285172   
>  .2052557
             Paraguay  |  -.0145634   .1208393    -0.12   0.904    -.2517875   
>  .2226607
   Dominican Republic  |   .0576552    .127854     0.45   0.652    -.1933396   
>  .3086499
                       |
              citysize |
           Large City  |   .0043135   .1705966     0.03   0.980    -.3305909   
>  .3392178
          Medium City  |  -.0646495   .1464671    -0.44   0.659    -.3521844   
>  .2228854
           Small City  |   .0273981   .1693094     0.16   0.871    -.3049794   
>  .3597755
           Rural Area  |  -.1524508   .1482869    -1.03   0.304    -.4435582   
>  .1386567
                       |
                  male |   .3148474   .0765593     4.11   0.000     .1645511   
>  .4651437
                   age |  -.0009651   .0026666    -0.36   0.718       -.0062   
>  .0042698
             education |   .0311158   .0102238     3.04   0.002     .0110452   
>  .0511865
               working |    .156461   .0768225     2.04   0.042     .0056479   
>  .3072741
voted_lastpresidential |   .0415992   .0894879     0.46   0.642    -.1340778   
>  .2172761
       householdincome |  -.0011148   .0084344    -0.13   0.895    -.0176726   
>  .0154431
        voteregistered |  -.1988492   .1516872    -1.31   0.190    -.4966318   
>  .0989335
               remesas |   .0142711   .0872032     0.16   0.870    -.1569207   
>  .1854628
                 _cons |   2.400366   .2251375    10.66   0.000     1.958391   
>  2.842342
-------------------------------------------------------------------------------
---------

. eststo m_2: reg  trustchina i.posttrump_7days##c.time_zero i.country i.citysi
> ze

      Source |       SS           df       MS      Number of obs   =       924
-------------+----------------------------------   F(10, 913)      =      1.83
       Model |  18.3068574        10  1.83068574   Prob > F        =    0.0511
    Residual |  911.332753       913  .998173881   R-squared       =    0.0197
-------------+----------------------------------   Adj R-squared   =    0.0090
       Total |   929.63961       923  1.00719351   Root MSE        =    .99909

-------------------------------------------------------------------------------
------
         trustchina |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |   .0057511    .151066     0.04   0.970    -.2907259    .3
> 022281
          time_zero |    .017449   .0257603     0.68   0.498    -.0331073    .0
> 680052
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0314228   .0359108    -0.88   0.382       -.1019    .0
> 390544
                    |
            country |
          Honduras  |  -.0584889   .1166645    -0.50   0.616    -.2874507    .1
> 704728
          Paraguay  |  -.0346309    .110029    -0.31   0.753    -.2505699    .1
> 813082
Dominican Republic  |   .0915317   .1224328     0.75   0.455    -.1487507    .3
> 318142
                    |
           citysize |
        Large City  |   .1056269   .1625891     0.65   0.516    -.2134649    .4
> 247186
       Medium City  |  -.0839766    .136876    -0.61   0.540    -.3526047    .1
> 846516
        Small City  |   .0260718   .1577692     0.17   0.869    -.2835607    .3
> 357042
        Rural Area  |  -.2276668   .1353469    -1.68   0.093     -.493294    .0
> 379604
                    |
              _cons |    2.81912   .1350327    20.88   0.000     2.554109    3.
> 084131
-------------------------------------------------------------------------------
------

. 
. //Trust in United Nations
. eststo m_3: reg trustun i.posttrump_7days i.country i.citysize male age educa
> tion working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,217
-------------+----------------------------------   F(16, 1200)     =      2.48
       Model |  28.5414948        16  1.78384343   Prob > F        =    0.0010
    Residual |  864.695974     1,200  .720579979   R-squared       =    0.0320
-------------+----------------------------------   Adj R-squared   =    0.0190
       Total |  893.237469     1,216  .734570287   Root MSE        =    .84887

-------------------------------------------------------------------------------
---------
               trustun |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0079767   .0521692     0.15   0.879    -.0943762   
>  .1103297
                       |
               country |
             Honduras  |    .136307   .0806743     1.69   0.091    -.0219715   
>  .2945854
             Paraguay  |  -.0414812   .0808238    -0.51   0.608    -.2000528   
>  .1170905
   Dominican Republic  |   .1832666   .0858337     2.14   0.033     .0148659   
>  .3516674
                       |
              citysize |
           Large City  |   .0363151   .1168363     0.31   0.756     -.192911   
>  .2655413
          Medium City  |   .0661403   .0975443     0.68   0.498     -.125236   
>  .2575167
           Small City  |   .0954509   .1077532     0.89   0.376    -.1159548   
>  .3068566
           Rural Area  |   .0505711   .0976858     0.52   0.605    -.1410829   
>   .242225
                       |
                  male |   .0865892   .0521368     1.66   0.097    -.0157003   
>  .1888787
                   age |   -.000664   .0018286    -0.36   0.717    -.0042515   
>  .0029236
             education |   .0195117   .0068953     2.83   0.005     .0059836   
>  .0330399
               working |  -.0371209   .0540384    -0.69   0.492    -.1431411   
>  .0688994
voted_lastpresidential |  -.0187181   .0613247    -0.31   0.760    -.1390336   
>  .1015975
       householdincome |   .0039103   .0058376     0.67   0.503    -.0075427   
>  .0153634
        voteregistered |   .0973897   .1071146     0.91   0.363    -.1127629   
>  .3075424
               remesas |   .0887507   .0611559     1.45   0.147    -.0312337   
>  .2087352
                 _cons |   2.470512   .1544112    16.00   0.000     2.167566   
>  2.773458
-------------------------------------------------------------------------------
---------

. eststo m_4: reg  trustun i.posttrump_7days##c.time_zero i.country i.citysize 
> male age education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,217
-------------+----------------------------------   F(18, 1198)     =      2.20
       Model |  28.6068981        18  1.58927212   Prob > F        =    0.0026
    Residual |  864.630571     1,198  .721728357   R-squared       =    0.0320
-------------+----------------------------------   Adj R-squared   =    0.0175
       Total |  893.237469     1,216  .734570287   Root MSE        =    .84955

-------------------------------------------------------------------------------
---------
               trustun |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0055423   .1110631     0.05   0.960    -.2123575   
>  .2234421
             time_zero |      -.003   .0195325    -0.15   0.878    -.0413218   
>  .0353218
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |   .0076931   .0265803     0.29   0.772    -.0444559   
>  .0598422
                       |
               country |
             Honduras  |    .134613   .0809739     1.66   0.097    -.0242534   
>  .2934794
             Paraguay  |  -.0415051   .0811848    -0.51   0.609    -.2007853   
>  .1177752
   Dominican Republic  |   .1830616   .0862519     2.12   0.034       .01384   
>  .3522831
                       |
              citysize |
           Large City  |   .0365475   .1169629     0.31   0.755    -.1929274   
>  .2660223
          Medium City  |   .0673641   .0978602     0.69   0.491    -.1246323   
>  .2593606
           Small City  |   .0956657   .1082796     0.88   0.377     -.116773   
>  .3081043
           Rural Area  |   .0521968   .0980214     0.53   0.594    -.1401158   
>  .2445095
                       |
                  male |   .0871709   .0522187     1.67   0.095    -.0152793   
>  .1896212
                   age |  -.0006668    .001831    -0.36   0.716    -.0042592   
>  .0029255
             education |   .0195399   .0069028     2.83   0.005      .005997   
>  .0330827
               working |  -.0380735   .0542228    -0.70   0.483    -.1444556   
>  .0683086
voted_lastpresidential |  -.0192651   .0614008    -0.31   0.754    -.1397302   
>  .1012001
       householdincome |   .0039596   .0058451     0.68   0.498    -.0075081   
>  .0154274
        voteregistered |   .0985267   .1073009     0.92   0.359    -.1119919   
>  .3090453
               remesas |   .0870351   .0614961     1.42   0.157    -.0336169   
>  .2076871
                 _cons |   2.457551   .1732137    14.19   0.000     2.117715   
>  2.797387
-------------------------------------------------------------------------------
---------

. 
. //Trust in Organization of American States
. eststo m_5: reg trustoas i.posttrump_7days i.country i.citysize male age educ
> ation working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,232
-------------+----------------------------------   F(16, 1215)     =      3.41
       Model |  38.3250358        16  2.39531474   Prob > F        =    0.0000
    Residual |  854.531295     1,215  .703317939   R-squared       =    0.0429
-------------+----------------------------------   Adj R-squared   =    0.0303
       Total |  892.856331     1,231  .725309773   Root MSE        =    .83864

-------------------------------------------------------------------------------
---------
              trustoas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0404638   .0514333     0.79   0.432    -.0604442   
>  .1413718
                       |
               country |
             Honduras  |   .1256523   .0831256     1.51   0.131    -.0374333   
>  .2887378
             Paraguay  |   .1448054    .082651     1.75   0.080    -.0173491   
>    .30696
   Dominican Republic  |   .3023219    .088066     3.43   0.001     .1295435   
>  .4751003
                       |
              citysize |
           Large City  |   .0514128   .1185431     0.43   0.665    -.1811592   
>  .2839848
          Medium City  |   .1490967   .1000634     1.49   0.136    -.0472195   
>   .345413
           Small City  |   .1162979   .1090603     1.07   0.286    -.0976696   
>  .3302653
           Rural Area  |  -.0052887   .0991288    -0.05   0.957    -.1997714   
>   .189194
                       |
                  male |   .0743851   .0515122     1.44   0.149    -.0266778   
>  .1754479
                   age |  -.0026089   .0017632    -1.48   0.139    -.0060681   
>  .0008503
             education |   .0057508   .0067592     0.85   0.395    -.0075101   
>  .0190118
               working |   -.087761   .0530359    -1.65   0.098    -.1918131   
>  .0162911
voted_lastpresidential |   .0347522   .0612204     0.57   0.570    -.0853573   
>  .1548617
       householdincome |  -.0013613   .0057098    -0.24   0.812    -.0125634   
>  .0098408
        voteregistered |    .159492   .1038535     1.54   0.125      -.04426   
>   .363244
               remesas |    .184613   .0615048     3.00   0.003     .0639456   
>  .3052805
                 _cons |    2.46507   .1523535    16.18   0.000     2.166165   
>  2.763975
-------------------------------------------------------------------------------
---------

. eststo m_6: reg trustoas i.posttrump_7days##c.time_zero i.country i.citysize 
> male age education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,232
-------------+----------------------------------   F(18, 1213)     =      3.07
       Model |   38.855692        18  2.15864955   Prob > F        =    0.0000
    Residual |  854.000639     1,213  .704040098   R-squared       =    0.0435
-------------+----------------------------------   Adj R-squared   =    0.0293
       Total |  892.856331     1,231  .725309773   Root MSE        =    .83907

-------------------------------------------------------------------------------
---------
              trustoas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |    .022634   .1081845     0.21   0.834    -.1896155   
>  .2348835
             time_zero |  -.0068664   .0184966    -0.37   0.711    -.0431553   
>  .0294226
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |   .0211026   .0257832     0.82   0.413     -.029482   
>  .0716873
                       |
               country |
             Honduras  |   .1207137   .0833713     1.45   0.148    -.0428543   
>  .2842818
             Paraguay  |   .1438882   .0829726     1.73   0.083    -.0188975   
>   .306674
   Dominican Republic  |   .3021889   .0884369     3.42   0.001     .1286827   
>  .4756952
                       |
              citysize |
           Large City  |   .0524979   .1187024     0.44   0.658    -.1803869   
>  .2853827
          Medium City  |   .1526921   .1003054     1.52   0.128    -.0440992   
>  .3494833
           Small City  |   .1195486   .1094753     1.09   0.275    -.0952334   
>  .3343305
           Rural Area  |   .0009038   .0995362     0.01   0.993    -.1943784   
>  .1961859
                       |
                  male |   .0753065   .0515499     1.46   0.144    -.0258305   
>  .1764434
                   age |  -.0026362   .0017651    -1.49   0.136    -.0060991   
>  .0008267
             education |   .0058916    .006765     0.87   0.384    -.0073809   
>  .0191641
               working |  -.0913916   .0532672    -1.72   0.086    -.1958977   
>  .0131144
voted_lastpresidential |   .0352041   .0612551     0.57   0.566    -.0849736   
>  .1553818
       householdincome |  -.0012748   .0057137    -0.22   0.823    -.0124846   
>   .009935
        voteregistered |   .1619239   .1040976     1.56   0.120    -.0423075   
>  .3661553
               remesas |   .1813514   .0617137     2.94   0.003     .0602741   
>  .3024288
                 _cons |   2.435103    .167792    14.51   0.000     2.105908   
>  2.764297
-------------------------------------------------------------------------------
---------

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure 5
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) ,  ///
>                         drop(*.country *.citysize male age householdincome ci
> tysize education working  voted_lastpresidential voteregistered remesas _cons
> ) xline(0, lpattern(solid)) byopts(row(1)) levels(95 90)      ///
>                         bylabels("A. Trust China, ± 7 days" "B. Trust UN, ± 7
>  days" "C. Trust OAS, ± 7 days") subtitle(, size(small)) nokey  ///
>                         coeflabel(1.posttrump_7days = "Treatment group" ///
>                         1.posttrump_7days#c.time_zero = "Treatment*Days" _con
> s = "Constant") ///
>                         aspect(.6) mlabgap(*2)

.                         
. addplot 1: , b1title("Effect on Trust", size(small)) norescaling

. addplot 2: , b1title("Effect on Trust") norescaling

. addplot 3: , b1title("Effect on Trust") norescaling

. 
. graph save Figure_placebo.gph, replace 
(file Figure_placebo.gph saved)

. graph export Figure_placebo.png, replace 
(file Figure_placebo.png written in PNG format)

. 
. *Generate accompanying table, which is Appendix Table B.7.
. 
. esttab m* using placebo_foreigngovs.tex, ///
>         drop(*.citysize *.country) unstack starlevels(+ .10 * 0.05 ** 0.01 **
> * 0.001) ///
>         cells(b(star fmt(%9.2f)) se(par)) stats(N, fmt(%9.0f %9.0g)) replace 
> label ///
>         mtitles("Trust in China" "\shortstack{Trust in China \\ with interact
> ion}" "Trust in UN" ///
>         "\shortstack{Trust in UN \\ with interaction}" "\shortstack{Trust in 
> OAS}" ///
>         "\shortstack{Trust in OAS \\ with interaction}") ///
>         nobaselevel collabels(, none) varlabels(_cons Constant) style(tex) pr
> ehead("\begin{tabular}{lcccccc}" ///
>         \hline\hline) posthead(\hline) prefoot(\hline) postfoot(\hline\hline 
> ///
>         \multicolumn{7}{l}{\footnotesize $+ p<0.10 * p<0.05 ** p<0.01 *** p<0
> .001.$ ///
>         Standard errors in parentheses. All models include country and size o
> f location fixed effects.}\\ "\end{tabular}" )
(output written to placebo_foreigngovs.tex)

.         
. drop sample_reg

. 
. *************************************
. *************************************
. //6. Appendix A: Tables and Figures//
. *************************************
. *************************************
. 
. *Table A.1. Interview Dates by Country
. 
. //Table A.1 is based on the following results: 
. sum fecha if paraguay==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       fecha |      1,528    20764.88    8.112992      20746      20782

. sum fecha if elsalv==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       fecha |      1,551    20769.99    10.49091      20753      20789

. sum fecha if hond==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       fecha |      1,560    20758.02    9.733179      20741      20778

. sum fecha if dr==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
       fecha |      1,518    20768.17    11.18491      20749      20791

. 
. *Figure A.1. Interviews per Day
. 
. //NOTE: Figure A.1. was created by hand using Adobe Illustrator,
. //based on histograms created in Stata. The code for the histograms is here:
. 
. hist fecha if paraguay==1, w(1)
(bin=36, start=20746, width=1)

. hist fecha if dr==1, w(1)
(bin=42, start=20749, width=1)

. hist fecha if elsalv==1, w(1)
(bin=36, start=20753, width=1)

. hist fecha if honduras==1, w(1)
(bin=37, start=20741, width=1)

. 
. *Table A.2 Summary Statistics
. 
. estpost summarize citysize age male ideology voteregistered voted_last trustc
> hina trustusgov trustoas trustun education working householdincome remesas em
> igrate 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)
>      e(max) 
-------------+-----------------------------------------------------------------
------------
    citysize |      6157       6157   3.214553   2.471341    1.57205          1
>           5 
         age |      6147       6147   39.53522   269.2198   16.40792         18
>         112 
        male |      6157       6157   .4978074   .2500358   .5000358          0
>           1 
    ideology |      5445       5445   5.552617   9.608408   3.099743          1
>          10 
voteregist~d |      6062       6062   .9176839   .0755526   .2748683          0
>           1 
voted_last~l |      6085       6085   .7089565   .2063711   .4542809          0
>           1 
  trustchina |      2265       2265   2.720088   .9640177    .981844          1
>           4 
  trustusgov |      3985       3985   2.884818   .8534467   .9238218          1
>           4 
    trustoas |      3454       3454   2.835553   .7577741   .8705022          1
>           4 
     trustun |      3535       3535   2.896747   .7524937   .8674639          1
>           4 
   education |      5985       5985   8.796825   19.74715   4.443776          0
>          18 
     working |      6157       6157   .4250447   .2444214   .4943899          0
>           1 
householdi~e |      5178       5178   7.567594   26.19873    5.11847          0
>          16 
     remesas |      6134       6134   .2021519   .1613128   .4016377          0
>           1 
    emigrate |      6091       6091   .3641438   .2315811   .4812288          0
>           1 

             |    e(sum) 
-------------+-----------
    citysize |     19792 
         age |    243023 
        male |      3065 
    ideology |     30234 
voteregist~d |      5563 
voted_last~l |      4314 
  trustchina |      6161 
  trustusgov |     11496 
    trustoas |      9794 
     trustun |     10240 
   education |     52649 
     working |      2617 
householdi~e |     39185 
     remesas |      1240 
    emigrate |      2218 

. esttab using summarystats.csv, nobaselevels b(2) se(2) cells("mean(fmt(2)) sd
> (fmt(2)) min(fmt(0)) max(fmt(0))")  mtitles("Mean" "Standard Deviation" "Mini
> mum" "Maximum") label nogaps replace 
(tabulating estimates stored by eststo; specify "." to tabulate the active resu
> lts)
(output written to summarystats.csv)

. 
. *Tables A.3, A.4, and A.5 and Figures A.2 and A.3 are based on the authors' a
> nalysis of Latin American media coverage
. 
. *Table A.6 Top 25 Google Search Terms by Country, Nov. 9, 2016
. 
. //Table A.6 reports the top Google search terms in each country on Nov. 9, 20
> 16.
. //The authors obtained this data from Google search trends. Google search tre
> nds are based on random sampling
. //of Google data, so the online interface returns slightly different results 
> each time it is queried.
. //The specific .csv files that were used to construct Table A.6 can be found 
> in the "Top Google Search terms"
. //folder in our replication package. There is a separate .csv file for each c
> ountry.
. 
. *Figure A.4 Daily Google Searches for "trump" and "clinton," Oct. 1 - Dec. 1,
>  2016
. 
. //Figure A.4 was created by hand using Excel and Adobe Illustrator. 
. //Figure A.4 is based on Google search data from Oct. 1 - Dec. 1, 2016. 
. //Google search trends are based on random sampling of Google data, so the on
> line 
. //interface returns slightly different results each time it is queried.
. //The specific .csv files used to construct this figure can be found in the "
> Google Searches for Clinton and Trump"
. //folder in our replication package. There is a separate .csv file for each c
> ountry. 
.  
. *Figure A.5 Estimated Percentages with a Good or Very Good Opinion of the US,
>  Latinobarometro Data
. 
. //Figure A.5 was made in Excel, based on summary statistics obtained from the
>  Latinobarometro online data 
. //tool. The underlying data and the graph can be access in the Excel file inc
> luded in our replication package.
. 
. 
. *Table A.7. No Relationship Between Size of Location and Trust in the US Gove
> rnment
. 
. mean trustusgov, over(citysize)

Mean estimation                   Number of obs   =      3,985

    _subpop_1: citysize = National Capital (Metropolitan a
    _subpop_2: citysize = Large City
    _subpop_3: citysize = Medium City
    _subpop_4: citysize = Small City
    _subpop_5: citysize = Rural Area

--------------------------------------------------------------
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
trustusgov   |
   _subpop_1 |   2.857838   .0281208      2.802706    2.912971
   _subpop_2 |   3.028112   .0402817      2.949138    3.107087
   _subpop_3 |    2.83697   .0346177        2.7691    2.904841
   _subpop_4 |   2.915718   .0412942      2.834758    2.996677
   _subpop_5 |    2.86876   .0266856      2.816441    2.921079
--------------------------------------------------------------

. esttab, cells(b(fmt(2)) ci(fmt(2) par) )   mtitles("Mean Estimated Level of T
> rust in the US Government" "95% confidence interval") label nogaps replace 
(tabulating estimates stored by eststo; specify "." to tabulate the active resu
> lts)

-------------------------------------------------------------------------------
-------------------
                              (1)          (2)          (3)          (4)       
>    (5)          (6)
                     Mean Estim~n 95% confid~l          m_3          m_4       
>    m_5          m_6
                           b/ci95       b/ci95       b/ci95       b/ci95       
> b/ci95       b/ci95
-------------------------------------------------------------------------------
-------------------
Post-Trump 7 Day W~0         0.00         0.00         0.00         0.00       
>   0.00         0.00
                      [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00
> ,0.00]  [0.00,0.00]
Post-Trump 7 Day W~1         0.02         0.01         0.01         0.01       
>   0.04         0.02
                     [-0.13,0.17] [-0.29,0.30] [-0.09,0.11] [-0.21,0.22] [-0.06
> ,0.14] [-0.19,0.23]
El Salvador                  0.00         0.00         0.00         0.00       
>   0.00         0.00
                      [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00
> ,0.00]  [0.00,0.00]
Honduras                    -0.04        -0.06         0.14         0.13       
>   0.13         0.12
                     [-0.29,0.21] [-0.29,0.17] [-0.02,0.29] [-0.02,0.29] [-0.04
> ,0.29] [-0.04,0.28]
Paraguay                    -0.01        -0.03        -0.04        -0.04       
>   0.14         0.14
                     [-0.25,0.22] [-0.25,0.18] [-0.20,0.12] [-0.20,0.12] [-0.02
> ,0.31] [-0.02,0.31]
Dominican Republic           0.06         0.09         0.18         0.18       
>   0.30         0.30
                     [-0.19,0.31] [-0.15,0.33]  [0.01,0.35]  [0.01,0.35]  [0.13
> ,0.48]  [0.13,0.48]
National Capital (~a         0.00         0.00         0.00         0.00       
>   0.00         0.00
                      [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00,0.00]  [0.00
> ,0.00]  [0.00,0.00]
Large City                   0.00         0.11         0.04         0.04       
>   0.05         0.05
                     [-0.33,0.34] [-0.21,0.42] [-0.19,0.27] [-0.19,0.27] [-0.18
> ,0.28] [-0.18,0.29]
Medium City                 -0.06        -0.08         0.07         0.07       
>   0.15         0.15
                     [-0.35,0.22] [-0.35,0.18] [-0.13,0.26] [-0.12,0.26] [-0.05
> ,0.35] [-0.04,0.35]
Small City                   0.03         0.03         0.10         0.10       
>   0.12         0.12
                     [-0.30,0.36] [-0.28,0.34] [-0.12,0.31] [-0.12,0.31] [-0.10
> ,0.33] [-0.10,0.33]
Rural Area                  -0.15        -0.23         0.05         0.05       
>  -0.01         0.00
                     [-0.44,0.14] [-0.49,0.04] [-0.14,0.24] [-0.14,0.24] [-0.20
> ,0.19] [-0.19,0.20]
Sex                          0.31                      0.09         0.09       
>   0.07         0.08
                      [0.16,0.47]              [-0.02,0.19] [-0.02,0.19] [-0.03
> ,0.18] [-0.03,0.18]
Age                         -0.00                     -0.00        -0.00       
>  -0.00        -0.00
                     [-0.01,0.00]              [-0.00,0.00] [-0.00,0.00] [-0.01
> ,0.00] [-0.01,0.00]
Years of Schooling           0.03                      0.02         0.02       
>   0.01         0.01
                      [0.01,0.05]               [0.01,0.03]  [0.01,0.03] [-0.01
> ,0.02] [-0.01,0.02]
Working                      0.16                     -0.04        -0.04       
>  -0.09        -0.09
                      [0.01,0.31]              [-0.14,0.07] [-0.14,0.07] [-0.19
> ,0.02] [-0.20,0.01]
Voted Last Preside~n         0.04                     -0.02        -0.02       
>   0.03         0.04
                     [-0.13,0.22]              [-0.14,0.10] [-0.14,0.10] [-0.09
> ,0.15] [-0.08,0.16]
Monthly Household ~e        -0.00                      0.00         0.00       
>  -0.00        -0.00
                     [-0.02,0.02]              [-0.01,0.02] [-0.01,0.02] [-0.01
> ,0.01] [-0.01,0.01]
Registered to Vote          -0.20                      0.10         0.10       
>   0.16         0.16
                     [-0.50,0.10]              [-0.11,0.31] [-0.11,0.31] [-0.04
> ,0.36] [-0.04,0.37]
Remittances                  0.01                      0.09         0.09       
>   0.18         0.18
                     [-0.16,0.19]              [-0.03,0.21] [-0.03,0.21]  [0.06
> ,0.31]  [0.06,0.30]
Days                                      0.02                     -0.00       
>               -0.01
                                  [-0.03,0.07]              [-0.04,0.04]       
>        [-0.04,0.03]
Post-Trump 7 Day W~s                      0.00                      0.00       
>                0.00
                                   [0.00,0.00]               [0.00,0.00]       
>         [0.00,0.00]
Post-Trump 7 Day W~s                     -0.03                      0.01       
>                0.02
                                  [-0.10,0.04]              [-0.04,0.06]       
>        [-0.03,0.07]
Constant                     2.40         2.82         2.47         2.46       
>   2.47         2.44
                      [1.96,2.84]  [2.55,3.08]  [2.17,2.77]  [2.12,2.80]  [2.17
> ,2.76]  [2.11,2.76]
-------------------------------------------------------------------------------
-------------------
Observations                  766          924         1217         1217       
>   1232         1232
-------------------------------------------------------------------------------
-------------------

. 
. *Table A.8 Size of location and trust in the US government
. 
. eststo clear

. eststo m_1: reg trustusgov citysize

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(1, 3983)      =      0.21
       Model |   .17903983         1   .17903983   Prob > F        =    0.6470
    Residual |  3399.95245     3,983  .853615981   R-squared       =    0.0001
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  3400.13149     3,984   .85344666   Root MSE        =    .92391

------------------------------------------------------------------------------
  trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    citysize |  -.0042359   .0092491    -0.46   0.647    -.0223693    .0138975
       _cons |    2.89792   .0321348    90.18   0.000     2.834918    2.960922
------------------------------------------------------------------------------

. eststo m_2: reg trustusgov citysize i.country

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(4, 3980)      =     21.47
       Model |  71.8162757         4  17.9540689   Prob > F        =    0.0000
    Residual |  3328.31522     3,980  .836260105   R-squared       =    0.0211
-------------+----------------------------------   Adj R-squared   =    0.0201
       Total |  3400.13149     3,984   .85344666   Root MSE        =    .91447

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
           citysize |  -.0070492    .009399    -0.75   0.453    -.0254764     .
> 011378
                    |
            country |
          Honduras  |   .2583565   .0392182     6.59   0.000     .1814668    .3
> 352462
          Paraguay  |  -.0000411   .0421026    -0.00   0.999    -.0825859    .0
> 825036
Dominican Republic  |   .2790839    .040436     6.90   0.000     .1998067     .
> 358361
                    |
              _cons |   2.768977   .0392621    70.53   0.000     2.692002    2.
> 845953
-------------------------------------------------------------------------------
------

. eststo m_3: reg trustusgov citysize i.country male age householdincome educat
> ion working  voted_lastpresidential voteregistered remesas 

      Source |       SS           df       MS      Number of obs   =     3,339
-------------+----------------------------------   F(12, 3326)     =      7.73
       Model |   76.391892        12    6.365991   Prob > F        =    0.0000
    Residual |  2737.44638     3,326  .823044613   R-squared       =    0.0271
-------------+----------------------------------   Adj R-squared   =    0.0236
       Total |  2813.83827     3,338  .842971323   Root MSE        =    .90722

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
              citysize |   .0115196   .0109806     1.05   0.294    -.0100098   
>  .0330489
                       |
               country |
             Honduras  |    .279663   .0424468     6.59   0.000     .1964385   
>  .3628875
             Paraguay  |  -.0031458   .0477943    -0.07   0.948     -.096855   
>  .0905633
   Dominican Republic  |    .262647   .0434798     6.04   0.000     .1773972   
>  .3478968
                       |
                  male |   .0637262   .0333885     1.91   0.056    -.0017378   
>  .1291902
                   age |  -.0003009    .001123    -0.27   0.789    -.0025027   
>  .0019009
       householdincome |   .0047781   .0036921     1.29   0.196    -.0024609   
>   .012017
             education |   .0067767   .0044257     1.53   0.126    -.0019008   
>  .0154541
               working |  -.0341896   .0346801    -0.99   0.324    -.1021861   
>  .0338069
voted_lastpresidential |   .0809368   .0391568     2.07   0.039     .0041629   
>  .1577107
        voteregistered |  -.0559359   .0697126    -0.80   0.422    -.1926197   
>   .080748
               remesas |   .0355795    .037941     0.94   0.348    -.0388105   
>  .1099696
                 _cons |   2.585045    .103851    24.89   0.000     2.381427   
>  2.788663
-------------------------------------------------------------------------------
---------

. 
. coefplot (m_1, label(Bivariate Model)) ///
> (m_2, label(+ Country Fixed Effects)) ///
> (m_3, label(+ Covariates)) ///
> , keep(citysize) xline(0)

. 
. graph export citysize_OLS.pdf, replace
(file /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroad/LAPOP
>  2016 original datasets/citysize_OLS.pdf written in PDF format)

.                 
. esttab using table1_citysize.csv, nobaselevels b(3) se(3) starlevels(* 0.05 *
> * 0.01  *** 0.001 ) mtitles("No Controls" "+Country FEs" "+Covariates") const
> ant label nogaps replace 
(output written to table1_citysize.csv)

. 
. *Figure A.6 Main Results with Province Fixed Effects 
. 
. //The code below repeats our main analysis (Figure 2) with fixed effects by p
> rovince. 
. //The results remain unchanged.
. 
. eststo clear

. //Panel A: Full Sample with Country FE and City Size FE and Province FE
. eststo m_1: reg trustusgov i.posttrump i.country i.citysize i.prov
note: 422.prov omitted because of collinearity
note: 1216.prov omitted because of collinearity
note: 2132.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(75, 3909)     =      3.72
       Model |  226.600715        75  3.02134287   Prob > F        =    0.0000
    Residual |  3173.53078     3,909  .811852335   R-squared       =    0.0666
-------------+----------------------------------   Adj R-squared   =    0.0487
       Total |  3400.13149     3,984   .85344666   Root MSE        =    .90103

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
        1.posttrump |  -.2907457   .0443797    -6.55   0.000    -.3777552   -.2
> 037362
                    |
            country |
          Honduras  |   .0807019   .1897344     0.43   0.671     -.291286    .4
> 526897
          Paraguay  |  -.2127768   .2730097    -0.78   0.436    -.7480318    .3
> 224782
Dominican Republic  |   .4574906   .1356487     3.37   0.001     .1915418    .7
> 234394
                    |
           citysize |
        Large City  |   .1444831   .0856326     1.69   0.092    -.0234057     .
> 312372
       Medium City  |   .1191928   .0755263     1.58   0.115    -.0288819    .2
> 672675
        Small City  |   .2001292   .0795027     2.52   0.012     .0442586    .3
> 559998
        Rural Area  |   .1733872   .0722776     2.40   0.016     .0316817    .3
> 150926
                    |
               prov |
               302  |   .2267311   .1381292     1.64   0.101    -.0440811    .4
> 975432
               303  |   .1110617   .1611754     0.69   0.491     -.204934    .4
> 270575
               304  |   .4222004   .1927386     2.19   0.029     .0443228     .
> 800078
               305  |   .1361326   .1424247     0.96   0.339    -.1431012    .4
> 153663
               306  |   .2243351   .1324613     1.69   0.090    -.0353646    .4
> 840349
               307  |   -.021177   .1683777    -0.13   0.900    -.3512934    .3
> 089395
               308  |   .0805173   .1705565     0.47   0.637    -.2538708    .4
> 149054
               309  |   .1975152   .1940089     1.02   0.309    -.1828531    .5
> 778835
               310  |   .1913374   .1763034     1.09   0.278     -.154318    .5
> 369928
               311  |   .0893569   .1675065     0.53   0.594    -.2390514    .4
> 177652
               312  |   .1325621   .1468899     0.90   0.367     -.155426    .4
> 205502
               313  |   .1187244   .1888285     0.63   0.530    -.2514872     .
> 488936
               314  |   .3291477   .1654232     1.99   0.047     .0048239    .6
> 534716
               401  |   .3709493   .1782363     2.08   0.037     .0215044    .7
> 203942
               402  |   .1999746   .1896127     1.05   0.292    -.1717746    .5
> 717237
               403  |   .2164574    .283387     0.76   0.445    -.3391429    .7
> 720578
               404  |   .2938832   .1669231     1.76   0.078    -.0333814    .6
> 211477
               405  |   .1529701   .1880402     0.81   0.416     -.215696    .5
> 216362
               406  |   .0703167    .219825     0.32   0.749    -.3606658    .5
> 012993
               407  |   .0266048   .1785301     0.15   0.882    -.3234161    .3
> 766258
               408  |  -.2265858   .2543348    -0.89   0.373    -.7252272    .2
> 720557
               409  |  -.0035386   .1937764    -0.02   0.985     -.383451    .3
> 763739
               410  |   -.052042   .2779569    -0.19   0.851    -.5969963    .4
> 929123
               411  |    .435866   .2366078     1.84   0.066    -.0280203    .8
> 997524
               412  |   .1680598   .2972622     0.57   0.572    -.4147439    .7
> 508634
               413  |   .1973773   .1982888     1.00   0.320    -.1913819    .5
> 861365
               418  |   .3206274   .2072352     1.55   0.122    -.0856721    .7
> 269268
               419  |  -.0007796   .2203047    -0.00   0.997    -.4327026    .4
> 311435
               420  |   .2160662   .2716236     0.80   0.426    -.3164712    .7
> 486036
               421  |    .447953   .2083154     2.15   0.032     .0395357    .8
> 563702
               422  |          0  (omitted)
          ASUNCION  |   .2832197   .2761412     1.03   0.305    -.2581747     .
> 824614
        CONCEPCION  |   .3724231   .3663349     1.02   0.309    -.3458024    1.
> 090649
         SAN PEDRO  |   .2578548    .291605     0.88   0.377    -.3138574    .8
> 295671
        CORDILLERA  |   .1432966   .3123367     0.46   0.646    -.4690617     .
> 755655
            GUAIRA  |   .2647294   .2818974     0.94   0.348    -.2879505    .8
> 174093
          CAAGUAZU  |   .1278991   .2727566     0.47   0.639    -.4068596    .6
> 626578
           CAAZAPA  |   .6406174   .3117605     2.05   0.040     .0293887    1.
> 251846
            ITAPUA  |   .3107633   .2895258     1.07   0.283    -.2568726    .8
> 783992
          MISIONES  |   .5582163    .312422     1.79   0.074    -.0543092    1.
> 170742
         PARAGUARI  |   .1830292   .3746353     0.49   0.625      -.55147    .9
> 175283
       ALTO PARANA  |  -.0087225   .2726031    -0.03   0.974    -.5431803    .5
> 257353
           CENTRAL  |   .4671038   .2616009     1.79   0.074    -.0457835     .
> 979991
          NEEMBUCU  |    .436173   .3208276     1.36   0.174    -.1928322    1.
> 065178
           AMAMBAY  |   .2449176   .3833631     0.64   0.523     -.506693    .9
> 965281
         CANINDEYU  |   .3269409   .3192053     1.02   0.306    -.2988838    .9
> 527657
        PDTE HAYES  |   .5798355   .3636289     1.59   0.111    -.1330848    1.
> 292756
          BOQUERON  |          0  (omitted)
              2101  |  -.0336882   .1049247    -0.32   0.748    -.2394006    .1
> 720241
              2102  |   .0521557   .2499376     0.21   0.835    -.4378648    .5
> 421762
              2103  |  -.3661993   .1984772    -1.85   0.065    -.7553279    .0
> 229293
              2106  |  -.0956452   .1991655    -0.48   0.631    -.4861234    .2
> 948329
              2108  |   .1887069   .2753338     0.69   0.493    -.3511046    .7
> 285185
              2109  |   .1000767   .2342949     0.43   0.669     -.359275    .5
> 594285
              2111  |    .217611   .1952444     1.11   0.265    -.1651795    .6
> 004015
              2112  |  -.1545345   .2668932    -0.58   0.563    -.6777976    .3
> 687285
              2113  |  -.0229246   .1404416    -0.16   0.870    -.2982703     .
> 252421
              2114  |  -.1307693   .2197783    -0.60   0.552    -.5616603    .3
> 001217
              2117  |   .0966897   .2498888     0.39   0.699     -.393235    .5
> 866145
              2118  |   .1342777   .2805583     0.48   0.632    -.4157769    .6
> 843322
              2119  |  -.1544303   .2364538    -0.65   0.514    -.6180147    .3
> 091541
              2120  |  -.1411294   .2563616    -0.55   0.582    -.6437447    .3
> 614858
              2121  |  -.1758118   .1401803    -1.25   0.210    -.4506453    .0
> 990217
              2122  |  -.1356938   .2752905    -0.49   0.622    -.6754205    .4
> 040328
              2123  |  -.1474287   .2048766    -0.72   0.472    -.5491038    .2
> 542465
              2124  |  -.0116502   .1822813    -0.06   0.949    -.3690257    .3
> 457252
              2125  |   .1575817   .1198822     1.31   0.189    -.0774558    .3
> 926193
              2127  |   .2617828    .249646     1.05   0.294    -.2276658    .7
> 512315
              2129  |   .0247563   .2156827     0.11   0.909     -.398105    .4
> 476175
              2132  |          0  (omitted)
                    |
              _cons |   2.637828   .1287617    20.49   0.000     2.385381    2.
> 890274
-------------------------------------------------------------------------------
------

. eststo m_2: reg trustusgov i.posttrump##c.time_zero i.country i.citysize i.pr
> ov
note: 422.prov omitted because of collinearity
note: 1216.prov omitted because of collinearity
note: 2132.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(77, 3907)     =      3.63
       Model |  227.032103        77  2.94846887   Prob > F        =    0.0000
    Residual |  3173.09939     3,907   .81215751   R-squared       =    0.0668
-------------+----------------------------------   Adj R-squared   =    0.0484
       Total |  3400.13149     3,984   .85344666   Root MSE        =     .9012

-------------------------------------------------------------------------------
--------
           trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. I
> nterval]
----------------------+--------------------------------------------------------
--------
          1.posttrump |  -.2489232   .0729225    -3.41   0.001     -.391893   -
> .1059535
            time_zero |  -.0015919   .0052764    -0.30   0.763    -.0119367    
> .0087529
                      |
posttrump#c.time_zero |
                   1  |  -.0016696   .0089921    -0.19   0.853    -.0192992    
> .0159601
                      |
              country |
            Honduras  |   .0668469   .1941216     0.34   0.731    -.3137424    
> .4474362
            Paraguay  |  -.2041155   .2733953    -0.75   0.455    -.7401264    
> .3318954
  Dominican Republic  |   .4688515   .1366684     3.43   0.001     .2009034    
> .7367996
                      |
             citysize |
          Large City  |   .1568788   .0876116     1.79   0.073      -.01489    
> .3286475
         Medium City  |   .1289319   .0785551     1.64   0.101     -.025081    
> .2829449
          Small City  |   .2098207   .0822638     2.55   0.011     .0485367    
> .3711047
          Rural Area  |   .1827535   .0756984     2.41   0.016     .0343415    
> .3311656
                      |
                 prov |
                 302  |   .2260562   .1381899     1.64   0.102     -.044875    
> .4969874
                 303  |   .1104637    .162175     0.68   0.496     -.207492    
> .4284194
                 304  |   .3985152   .2007622     1.99   0.047     .0049066    
> .7921239
                 305  |   .1460021   .1449089     1.01   0.314    -.1381021    
> .4301062
                 306  |   .2370634   .1391642     1.70   0.089     -.035778    
> .5099047
                 307  |  -.0229321   .1732328    -0.13   0.895    -.3625673    
> .3167032
                 308  |   .0688252   .1741503     0.40   0.693    -.2726088    
> .4102592
                 309  |   .2009962   .1945532     1.03   0.302    -.1804393    
> .5824317
                 310  |   .1968119   .1768275     1.11   0.266    -.1498711    
> .5434949
                 311  |   .1311761   .1800505     0.73   0.466    -.2218257    
> .4841779
                 312  |   .1513397   .1491871     1.01   0.310    -.1411522    
> .4438316
                 313  |   .1418044   .1917764     0.74   0.460    -.2341869    
> .5177957
                 314  |   .3519852   .1686983     2.09   0.037     .0212401    
> .6827303
                 401  |   .3749332   .1784124     2.10   0.036     .0251431    
> .7247234
                 402  |   .2026784   .1898964     1.07   0.286    -.1696272    
> .5749839
                 403  |   .2133668   .2835572     0.75   0.452    -.3425673    
> .7693009
                 404  |   .3013122   .1702062     1.77   0.077    -.0323893    
> .6350137
                 405  |   .1536553   .2005008     0.77   0.444    -.2394409    
> .5467514
                 406  |    .080664   .2347197     0.34   0.731    -.3795206    
> .5408487
                 407  |   .0446776   .1925392     0.23   0.817    -.3328093    
> .4221646
                 408  |   -.223668   .2629369    -0.85   0.395    -.7391746    
> .2918386
                 409  |      .0203   .2095488     0.10   0.923    -.3905353    
> .4311353
                 410  |  -.0468895   .2854072    -0.16   0.870    -.6064507    
> .5126718
                 411  |   .4324763   .2478566     1.74   0.081    -.0534642    
> .9184168
                 412  |   .1582121   .3085132     0.51   0.608    -.4466501    
> .7630743
                 413  |    .210444   .2030212     1.04   0.300    -.1875936    
> .6084815
                 418  |    .332553   .2111479     1.57   0.115    -.0814176    
> .7465235
                 419  |   .0210587   .2320814     0.09   0.928    -.4339535    
>  .476071
                 420  |   .2260651   .2781245     0.81   0.416    -.3192178    
>  .771348
                 421  |   .4491992   .2084134     2.16   0.031     .0405899    
> .8578086
                 422  |          0  (omitted)
            ASUNCION  |   .2838795    .276203     1.03   0.304    -.2576361    
> .8253952
          CONCEPCION  |   .3767014   .3686702     1.02   0.307    -.3461027    
> 1.099506
           SAN PEDRO  |   .2464351   .2938547     0.84   0.402    -.3296881    
> .8225583
          CORDILLERA  |   .1377779   .3126457     0.44   0.659    -.4751862    
>  .750742
              GUAIRA  |   .2555941   .2838334     0.90   0.368    -.3008815    
> .8120697
            CAAGUAZU  |   .1093765   .2749407     0.40   0.691    -.4296643    
> .6484173
             CAAZAPA  |   .6186036   .3144462     1.97   0.049     .0021094    
> 1.235098
              ITAPUA  |   .3053902     .29126     1.05   0.294    -.2656458    
> .8764262
            MISIONES  |   .5607991   .3140213     1.79   0.074    -.0548621    
>  1.17646
           PARAGUARI  |   .1823991    .375579     0.49   0.627    -.5539504    
> .9187486
         ALTO PARANA  |   -.015333   .2754024    -0.06   0.956     -.555279    
>  .524613
             CENTRAL  |   .4612879   .2618531     1.76   0.078    -.0520939    
> .9746696
            NEEMBUCU  |   .4321666   .3237929     1.33   0.182    -.2026526    
> 1.066986
             AMAMBAY  |   .2475459    .385036     0.64   0.520    -.5073447    
> 1.002436
           CANINDEYU  |   .3054587    .321722     0.95   0.342    -.3253002    
> .9362177
          PDTE HAYES  |   .5714344   .3640486     1.57   0.117     -.142309    
> 1.285178
            BOQUERON  |          0  (omitted)
                2101  |  -.0236087   .1060828    -0.22   0.824    -.2315916    
> .1843742
                2102  |   .0504179   .2519789     0.20   0.841    -.4436048    
> .5444406
                2103  |  -.3730187   .1993722    -1.87   0.061    -.7639021    
> .0178648
                2106  |  -.1012218    .201163    -0.50   0.615    -.4956162    
> .2931726
                2108  |   .2097785   .2782324     0.75   0.451     -.335716    
>  .755273
                2109  |   .0804698   .2374535     0.34   0.735    -.3850747    
> .5460144
                2111  |   .2257058   .1956386     1.15   0.249    -.1578576    
> .6092691
                2112  |   -.138289   .2686471    -0.51   0.607    -.6649908    
> .3884129
                2113  |  -.0463646   .1485005    -0.31   0.755    -.3375104    
> .2447812
                2114  |  -.1363751   .2207811    -0.62   0.537    -.5692322    
> .2964821
                2117  |   .0871509   .2556325     0.34   0.733    -.4140348    
> .5883367
                2118  |   .1091053   .2849679     0.38   0.702    -.4495945    
> .6678052
                2119  |  -.1684197   .2377269    -0.71   0.479    -.6345003    
> .2976609
                2120  |   -.148593    .256963    -0.58   0.563    -.6523874    
> .3552014
                2121  |  -.2040098   .1491809    -1.37   0.172    -.4964897    
> .0884701
                2122  |  -.1400538   .2764012    -0.51   0.612    -.6819581    
> .4018505
                2123  |  -.1336234    .206409    -0.65   0.517    -.5383029    
> .2710561
                2124  |  -.0210776   .1830344    -0.12   0.908    -.3799296    
> .3377744
                2125  |   .1404981   .1279717     1.10   0.272    -.1103996    
> .3913958
                2127  |   .2346916   .2543469     0.92   0.356    -.2639736    
> .7333568
                2129  |   .0321422   .2161918     0.15   0.882    -.3917173    
> .4560016
                2132  |          0  (omitted)
                      |
                _cons |   2.612098   .1521606    17.17   0.000     2.313776    
> 2.910419
-------------------------------------------------------------------------------
--------

. local n1 = `e(N)'

. 
. //Panel B: 7 day bandwidth with Country FE and City Size FE and Province FE
. eststo m_3: reg trustusgov i.posttrump_7days i.country i.citysize i.prov
note: 420.prov omitted because of collinearity
note: 1214.prov omitted because of collinearity
note: 2127.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(52, 1577)     =      2.31
       Model |  96.3428388        52   1.8527469   Prob > F        =    0.0000
    Residual |  1264.90624     1,577  .802096538   R-squared       =    0.0708
-------------+----------------------------------   Adj R-squared   =    0.0401
       Total |  1361.24908     1,629  .835634794   Root MSE        =     .8956

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.2283539   .0777223    -2.94   0.003    -.3808038    -.
> 075904
                    |
            country |
          Honduras  |   .4857478   .3721071     1.31   0.192    -.2441289    1.
> 215624
          Paraguay  |  -.0326502   .2741468    -0.12   0.905    -.5703807    .5
> 050803
Dominican Republic  |   .5640765   .2999542     1.88   0.060    -.0242744    1.
> 152427
                    |
           citysize |
        Large City  |   .0798383   .1937199     0.41   0.680    -.3001375     .
> 459814
       Medium City  |   .1312258   .1640792     0.80   0.424    -.1906104     .
> 453062
        Small City  |   .1861332   .1648724     1.13   0.259     -.137259    .5
> 095255
        Rural Area  |    .135765   .1645725     0.82   0.410    -.1870389     .
> 458569
                    |
               prov |
               304  |   .3181412   .2511768     1.27   0.205    -.1745343    .8
> 108167
               305  |   .0795002   .3248259     0.24   0.807    -.5576359    .7
> 166364
               306  |   .1007814   .2570143     0.39   0.695    -.4033443     .
> 604907
               307  |  -.1198267   .2354088    -0.51   0.611    -.5815738    .3
> 419205
               308  |   .0156619   .2546048     0.06   0.951    -.4837378    .5
> 150615
               309  |   .1459221   .2671226     0.55   0.585    -.3780307    .6
> 698749
               310  |  -.0120465   .2729736    -0.04   0.965    -.5474758    .5
> 233829
               401  |  -.5284153    .490167    -1.08   0.281    -1.489863    .4
> 330323
               404  |  -.3248593   .3688842    -0.88   0.379    -1.048414    .3
> 986958
               405  |  -.3480117   .3339379    -1.04   0.298    -1.003021    .3
> 069973
               406  |   -.438115    .355928    -1.23   0.219    -1.136257     .
> 260027
               407  |  -.4793162   .3427448    -1.40   0.162      -1.1516    .1
> 929673
               408  |  -.7560401   .3725592    -2.03   0.043    -1.486804   -.0
> 252766
               409  |  -.4800259    .347091    -1.38   0.167    -1.160834    .2
> 007824
               410  |  -.5876841    .389752    -1.51   0.132    -1.352171    .1
> 768026
               411  |   -.087963   .3605126    -0.24   0.807    -.7950974    .6
> 191715
               412  |  -.3557692   .4024448    -0.88   0.377    -1.145152    .4
> 336139
               413  |  -1.353354   .9530999    -1.42   0.156     -3.22283    .5
> 161223
               419  |   -.477907   .3622312    -1.32   0.187    -1.188412    .2
> 325983
               420  |          0  (omitted)
        CONCEPCION  |   .0862499    .335419     0.26   0.797    -.5716643     .
> 744164
         SAN PEDRO  |   -.020852   .2493305    -0.08   0.933    -.5099062    .4
> 682022
        CORDILLERA  |  -.2018682   .4233154    -0.48   0.634    -1.032188    .6
> 284521
            GUAIRA  |   .0088222   .2486965     0.04   0.972    -.4789883    .4
> 966327
          CAAGUAZU  |   -.206436   .2277575    -0.91   0.365    -.6531754    .2
> 403034
           CAAZAPA  |   .4433556   .2839436     1.56   0.119    -.1135911    1.
> 000302
            ITAPUA  |   .0301507   .2465898     0.12   0.903    -.4535276     .
> 513829
          MISIONES  |   .3044279   .2802314     1.09   0.277    -.2452375    .8
> 540933
         PARAGUARI  |  -.1031441    .344358    -0.30   0.765    -.7785917    .5
> 723035
       ALTO PARANA  |  -.2627759   .2268035    -1.16   0.247    -.7076441    .1
> 820923
           CENTRAL  |   .2879843   .2561496     1.12   0.261    -.2144452    .7
> 904138
          NEEMBUCU  |   .1591332   .2826543     0.56   0.574    -.3952846     .
> 713551
           AMAMBAY  |  -.0313246   .3537817    -0.09   0.929    -.7252566    .6
> 626073
         CANINDEYU  |          0  (omitted)
              2102  |  -.1604768   .3367331    -0.48   0.634    -.8209685    .5
> 000149
              2103  |  -.5256628   .3363046    -1.56   0.118    -1.185314    .1
> 339884
              2106  |  -.1947307    .307402    -0.63   0.527    -.7976903    .4
> 082289
              2109  |  -.1042746   .3242914    -0.32   0.748    -.7403622     .
> 531813
              2113  |   -.208574   .2584853    -0.81   0.420    -.7155849    .2
> 984369
              2114  |  -.3285052   .3156128    -1.04   0.298    -.9475701    .2
> 905596
              2117  |  -.0631087   .3365165    -0.19   0.851    -.7231756    .5
> 969582
              2118  |  -.0640687   .3621442    -0.18   0.860    -.7744034     .
> 646266
              2120  |   .0683173    .511945     0.13   0.894    -.9358471    1.
> 072482
              2121  |  -.4718217    .261548    -1.80   0.071    -.9848401    .0
> 411967
              2122  |  -.3441884   .3557612    -0.97   0.333    -1.042003    .3
> 536263
              2125  |  -.0296264   .2534965    -0.12   0.907     -.526852    .4
> 675993
              2127  |          0  (omitted)
                    |
              _cons |   2.731841   .2686092    10.17   0.000     2.204972     3
> .25871
-------------------------------------------------------------------------------
------

. eststo m_4: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e i.prov
note: 420.prov omitted because of collinearity
note: 1214.prov omitted because of collinearity
note: 2127.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(54, 1575)     =      2.25
       Model |  97.5411714        54  1.80631799   Prob > F        =    0.0000
    Residual |  1263.70791     1,575  .802354228   R-squared       =    0.0717
-------------+----------------------------------   Adj R-squared   =    0.0398
       Total |  1361.24908     1,629  .835634794   Root MSE        =    .89574

-------------------------------------------------------------------------------
------
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.2006197   .1187118    -1.69   0.091    -.4334694    .0
> 322301
          time_zero |   .0107584   .0213889     0.50   0.615    -.0311953    .0
> 527121
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0378939    .032484    -1.17   0.244    -.1016103    .0
> 258224
                    |
            country |
          Honduras  |   .5859786   .3828946     1.53   0.126    -.1650582    1.
> 337015
          Paraguay  |   .0193779   .2782228     0.07   0.944     -.526348    .5
> 651039
Dominican Republic  |    .592737   .3010916     1.97   0.049     .0021543     1
> .18332
                    |
           citysize |
        Large City  |   .0976467    .196396     0.50   0.619    -.2875785     .
> 482872
       Medium City  |    .138647   .1656737     0.84   0.403    -.1863172    .4
> 636111
        Small City  |   .1960831   .1672621     1.17   0.241    -.1319966    .5
> 241628
        Rural Area  |   .1402562   .1664193     0.84   0.399    -.1861704    .4
> 666828
                    |
               prov |
               304  |   .2638405   .2557561     1.03   0.302    -.2378178    .7
> 654988
               305  |   .1147306   .3296163     0.35   0.728    -.5318023    .7
> 612635
               306  |   .1295435   .2591851     0.50   0.617    -.3788407    .6
> 379276
               307  |  -.1159321   .2361008    -0.49   0.623    -.5790371    .3
> 471729
               308  |    .004494   .2548188     0.02   0.986    -.4953258    .5
> 043138
               309  |   .2275371   .2769719     0.82   0.411    -.3157354    .7
> 708097
               310  |   .0973677   .2902615     0.34   0.737     -.471972    .6
> 667074
               401  |  -.5789193   .5029418    -1.15   0.250    -1.565425    .4
> 075865
               404  |   -.407708   .3785727    -1.08   0.282    -1.150267    .3
> 348514
               405  |  -.4050075   .3378556    -1.20   0.231    -1.067701    .2
> 576865
               406  |  -.5558174    .370006    -1.50   0.133    -1.281574    .1
> 699388
               407  |  -.5692295   .3541263    -1.61   0.108    -1.263838     .
> 125379
               408  |  -.7885133   .3737175    -2.11   0.035    -1.521549   -.0
> 554771
               409  |  -.5645606   .3567763    -1.58   0.114    -1.264367    .1
> 352459
               410  |  -.6022853   .3900094    -1.54   0.123    -1.367278     .
> 162707
               411  |   -.172259   .3683716    -0.47   0.640    -.8948094    .5
> 502915
               412  |  -.4937951   .4210224    -1.17   0.241    -1.319619    .3
> 320283
               413  |  -1.402948   .9598584    -1.46   0.144    -3.285683    .4
> 797866
               419  |  -.5483573   .3734291    -1.47   0.142    -1.280828    .1
> 841131
               420  |          0  (omitted)
        CONCEPCION  |   .0272398    .339039     0.08   0.936    -.6377755    .6
> 922552
         SAN PEDRO  |  -.0770584   .2565762    -0.30   0.764    -.5803252    .4
> 262084
        CORDILLERA  |  -.2068988   .4321387    -0.48   0.632    -1.054526    .6
> 407288
            GUAIRA  |  -.0597134   .2555916    -0.23   0.815    -.5610489    .4
> 416221
          CAAGUAZU  |   -.193457   .2280798    -0.85   0.396    -.6408289    .2
> 539149
           CAAZAPA  |   .3952057   .2873001     1.38   0.169    -.1683253    .9
> 587366
            ITAPUA  |   .0000502   .2484777     0.00   1.000    -.4873317     .
> 487432
          MISIONES  |   .2615286   .2829721     0.92   0.356    -.2935131    .8
> 165703
         PARAGUARI  |  -.1289824   .3488849    -0.37   0.712    -.8133102    .5
> 553454
       ALTO PARANA  |  -.3027315   .2292131    -1.32   0.187    -.7523264    .1
> 468635
           CENTRAL  |   .2563073   .2616887     0.98   0.328    -.2569875    .7
> 696021
          NEEMBUCU  |   .0833998   .2895493     0.29   0.773    -.4845428    .6
> 513424
           AMAMBAY  |  -.0780938    .356106    -0.22   0.826    -.7765855     .
> 620398
         CANINDEYU  |          0  (omitted)
              2102  |  -.1737059    .337671    -0.51   0.607    -.8360378     .
> 488626
              2103  |  -.5091156   .3444061    -1.48   0.140    -1.184658    .1
> 664271
              2106  |  -.1905787   .3082974    -0.62   0.537    -.7952951    .4
> 141377
              2109  |  -.0328863   .3313001    -0.10   0.921    -.6827221    .6
> 169494
              2113  |  -.2250956    .258928    -0.87   0.385    -.7329754    .2
> 827842
              2114  |  -.3139593   .3235314    -0.97   0.332    -.9485569    .3
> 206382
              2117  |  -.1257137   .3413057    -0.37   0.713     -.795175    .5
> 437476
              2118  |  -.0363431    .363471    -0.10   0.920    -.7492809    .6
> 765948
              2120  |   .0902935   .5182475     0.17   0.862    -.9262343    1.
> 106821
              2121  |  -.4975266   .2625013    -1.90   0.058    -1.012415    .0
> 173623
              2122  |  -.3392426   .3599418    -0.94   0.346    -1.045258    .3
> 667729
              2125  |   -.060852   .2559375    -0.24   0.812    -.5628661    .4
> 411621
              2127  |          0  (omitted)
                    |
              _cons |   2.752022   .2727986    10.09   0.000     2.216936    3.
> 287109
-------------------------------------------------------------------------------
------

. local n3 = `e(N)'

. 
. //Panel C: 7 day bandwidth with Country FE and City Size FE and Province FE p
> lus covariate adjustment
. eststo m_5: reg trustusgov i.posttrump_7days i.country i.citysize i.prov male
>  age householdincome education working  voted_lastpresidential voteregistered
>  remesas 
note: 420.prov omitted because of collinearity
note: 1214.prov omitted because of collinearity
note: 2127.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(59, 1314)     =      2.16
       Model |  98.6999037        59  1.67287972   Prob > F        =    0.0000
    Residual |  1016.62542     1,314  .773687537   R-squared       =    0.0885
-------------+----------------------------------   Adj R-squared   =    0.0476
       Total |  1115.32533     1,373   .81232726   Root MSE        =     .8796

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.2718253   .0823836    -3.30   0.001    -.4334432   
> -.1102075
                       |
               country |
             Honduras  |   .3930804   .3945673     1.00   0.319    -.3809702   
>  1.167131
             Paraguay  |   .0010693   .3066728     0.00   0.997    -.6005525   
>  .6026911
   Dominican Republic  |   .5996732   .3175063     1.89   0.059    -.0232014   
>  1.222548
                       |
              citysize |
           Large City  |  -.1316313   .2075336    -0.63   0.526    -.5387647   
>   .275502
          Medium City  |   .0111584   .1766649     0.06   0.950    -.3354176   
>  .3577344
           Small City  |   .0552923   .1774904     0.31   0.755    -.2929032   
>  .4034877
           Rural Area  |   .0835284   .1780126     0.47   0.639    -.2656916   
>  .4327483
                       |
                  prov |
                  304  |   .3979444   .2695175     1.48   0.140    -.1307872   
>  .9266761
                  305  |   .1303827   .3451302     0.38   0.706    -.5466838   
>  .8074492
                  306  |    .052014   .2739283     0.19   0.849    -.4853705   
>  .5893986
                  307  |  -.0685338   .2489743    -0.28   0.783    -.5569644   
>  .4198969
                  308  |    .069995   .2635507     0.27   0.791    -.4470311   
>  .5870211
                  309  |   .1883669   .2907921     0.65   0.517    -.3821007   
>  .7588344
                  310  |   .1217468   .2859768     0.43   0.670    -.4392742   
>  .6827679
                  401  |  -.4431338    .560176    -0.79   0.429    -1.542071   
>  .6558032
                  404  |  -.1935877   .3884049    -0.50   0.618    -.9555491   
>  .5683736
                  405  |  -.0626427   .3542282    -0.18   0.860    -.7575573   
>  .6322719
                  406  |  -.2400021   .3824353    -0.63   0.530    -.9902527   
>  .5102484
                  407  |  -.3404385    .362614    -0.94   0.348    -1.051804   
>  .3709271
                  408  |   -.577486   .4027075    -1.43   0.152    -1.367506   
>  .2125339
                  409  |   -.237236   .3679019    -0.64   0.519    -.9589753   
>  .4845034
                  410  |  -.3808039   .4164879    -0.91   0.361    -1.197858   
>  .4362501
                  411  |   .2359767   .3807626     0.62   0.536    -.5109924   
>  .9829457
                  412  |  -.1959919   .4214486    -0.47   0.642    -1.022778   
>  .6307937
                  419  |   .0164462   .4009729     0.04   0.967    -.7701708   
>  .8030631
                  420  |          0  (omitted)
           CONCEPCION  |   .1316165   .3539177     0.37   0.710     -.562689   
>   .825922
            SAN PEDRO  |   .0787207    .279679     0.28   0.778    -.4699455   
>  .6273868
           CORDILLERA  |   -.147244   .4346615    -0.34   0.735    -.9999502   
>  .7054623
               GUAIRA  |    .093957   .2846459     0.33   0.741     -.464453   
>  .6523671
             CAAGUAZU  |  -.2223018   .2659349    -0.84   0.403    -.7440052   
>  .2994016
              CAAZAPA  |   .4546268   .3369108     1.35   0.177     -.206315   
>  1.115569
               ITAPUA  |  -.0721501   .2857981    -0.25   0.801    -.6328204   
>  .4885202
             MISIONES  |   .2154664   .3249125     0.66   0.507    -.4219375   
>  .8528703
            PARAGUARI  |  -.1808205    .413711    -0.44   0.662    -.9924268   
>  .6307858
          ALTO PARANA  |  -.1767305   .2621516    -0.67   0.500    -.6910119   
>   .337551
              CENTRAL  |   .2979352   .2874099     1.04   0.300    -.2658973   
>  .8617676
             NEEMBUCU  |   .3128332   .3411987     0.92   0.359    -.3565205   
>   .982187
              AMAMBAY  |   .0682518   .3817827     0.18   0.858    -.6807184   
>   .817222
            CANINDEYU  |          0  (omitted)
                 2102  |  -.2139477   .3559906    -0.60   0.548    -.9123198   
>  .4844243
                 2103  |  -.6488008   .3565323    -1.82   0.069    -1.348236   
>   .050634
                 2106  |  -.1887907   .3178025    -0.59   0.553    -.8122464   
>   .434665
                 2109  |   .0359255   .3336209     0.11   0.914    -.6185622   
>  .6904132
                 2113  |  -.1049899   .2717589    -0.39   0.699    -.6381185   
>  .4281388
                 2114  |  -.2142134   .3394686    -0.63   0.528     -.880173   
>  .4517462
                 2117  |   -.054665   .3709227    -0.15   0.883    -.7823304   
>  .6730004
                 2118  |   .1068316   .3697582     0.29   0.773    -.6185494   
>  .8322126
                 2120  |   .0385215     .51295     0.08   0.940    -.9677688   
>  1.044812
                 2121  |   -.449303     .27975    -1.61   0.108    -.9981085   
>  .0995025
                 2122  |  -.1735619   .3799079    -0.46   0.648    -.9188542   
>  .5717304
                 2125  |   .0360419   .2674272     0.13   0.893     -.488589   
>  .5606729
                 2127  |          0  (omitted)
                       |
                  male |    .121744   .0509989     2.39   0.017     .0216958   
>  .2217922
                   age |   .0000325   .0017753     0.02   0.985    -.0034502   
>  .0035152
       householdincome |   .0035995   .0058942     0.61   0.542    -.0079635   
>  .0151625
             education |   .0085145    .006916     1.23   0.218    -.0050532   
>  .0220821
               working |  -.0185605   .0538366    -0.34   0.730    -.1241755   
>  .0870546
voted_lastpresidential |   .0669437   .0617482     1.08   0.279    -.0541921   
>  .1880794
        voteregistered |   -.141677   .1055698    -1.34   0.180    -.3487807   
>  .0654267
               remesas |   .0450435   .0605557     0.74   0.457    -.0737529   
>  .1638399
                 _cons |   2.678978   .3194999     8.38   0.000     2.052192   
>  3.305763
-------------------------------------------------------------------------------
---------

. eststo m_6: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e i.prov male age householdincome education working  voted_lastpresidential v
> oteregistered remesas 
note: 420.prov omitted because of collinearity
note: 1214.prov omitted because of collinearity
note: 2127.prov omitted because of collinearity

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(61, 1312)     =      2.11
       Model |  99.5202935        61  1.63148022   Prob > F        =    0.0000
    Residual |  1015.80503     1,312  .774241642   R-squared       =    0.0892
-------------+----------------------------------   Adj R-squared   =    0.0469
       Total |  1115.32533     1,373   .81232726   Root MSE        =    .87991

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1897135   .1280763    -1.48   0.139    -.4409702   
>  .0615432
             time_zero |  -.0057718   .0229091    -0.25   0.801    -.0507143   
>  .0391708
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |   -.019937   .0346499    -0.58   0.565    -.0879124   
>  .0480383
                       |
               country |
             Honduras  |    .490842   .4067195     1.21   0.228    -.3070497   
>  1.288734
             Paraguay  |   .0489944   .3104798     0.16   0.875    -.5600968   
>  .6580856
   Dominican Republic  |   .6263787   .3187354     1.97   0.050     .0010919   
>  1.251666
                       |
              citysize |
           Large City  |  -.1368443   .2102549    -0.65   0.515    -.5493168   
>  .2756283
          Medium City  |   .0016964   .1784738     0.01   0.992    -.3484288   
>  .3518217
           Small City  |   .0434703   .1801392     0.24   0.809    -.3099221   
>  .3968626
           Rural Area  |   .0707338   .1799486     0.39   0.694    -.2822846   
>  .4237523
                       |
                  prov |
                  304  |   .3476259   .2743932     1.27   0.205    -.1906715   
>  .8859233
                  305  |   .1256523   .3505836     0.36   0.720    -.5621135   
>   .813418
                  306  |   .0600243   .2764684     0.22   0.828    -.4823441   
>  .6023927
                  307  |  -.0497978   .2499812    -0.20   0.842    -.5402044   
>  .4406088
                  308  |   .0607183   .2638299     0.23   0.818    -.4568563   
>  .5782929
                  309  |   .2670356   .3013393     0.89   0.376    -.3241239   
>  .8581951
                  310  |   .2261504   .3044649     0.74   0.458    -.3711407   
>  .8234416
                  401  |  -.5494073   .5730397    -0.96   0.338    -1.673582   
>   .574767
                  404  |  -.2846953   .3985624    -0.71   0.475    -1.066585   
>  .4971939
                  405  |  -.1198066   .3590541    -0.33   0.739    -.8241895   
>  .5845763
                  406  |   -.345223   .3981166    -0.87   0.386    -1.126238   
>  .4357917
                  407  |  -.4363655   .3746064    -1.16   0.244    -1.171259   
>  .2985275
                  408  |  -.6139598   .4045717    -1.52   0.129    -1.407638   
>  .1797184
                  409  |   -.326538   .3782436    -0.86   0.388    -1.068566   
>  .4154904
                  410  |  -.3953626   .4169219    -0.95   0.343    -1.213269   
>   .422544
                  411  |   .1499921   .3905347     0.38   0.701    -.6161486   
>  .9161328
                  412  |  -.3313757   .4429814    -0.75   0.455    -1.200405   
>  .5376536
                  419  |   -.080073   .4122863    -0.19   0.846    -.8888855   
>  .7287395
                  420  |          0  (omitted)
           CONCEPCION  |   .1026724   .3571344     0.29   0.774    -.5979445   
>  .8032893
            SAN PEDRO  |   .0155005   .2864561     0.05   0.957    -.5464616   
>  .5774626
           CORDILLERA  |  -.2037594   .4439634    -0.46   0.646    -1.074715   
>  .6671962
               GUAIRA  |   .0412795   .2901574     0.14   0.887    -.5279437   
>  .6105026
             CAAGUAZU  |  -.2113384   .2662652    -0.79   0.428    -.7336904   
>  .3110136
              CAAZAPA  |   .4044561   .3407111     1.19   0.235     -.263942   
>  1.072854
               ITAPUA  |  -.1016042   .2873338    -0.35   0.724    -.6652881   
>  .4620798
             MISIONES  |   .1785858   .3270704     0.55   0.585    -.4630522   
>  .8202238
            PARAGUARI  |   -.229716   .4176445    -0.55   0.582     -1.04904   
>   .589608
          ALTO PARANA  |  -.2039547   .2640734    -0.77   0.440     -.722007   
>  .3140977
              CENTRAL  |   .2497173   .2918394     0.86   0.392    -.3228056   
>  .8222402
             NEEMBUCU  |   .2775584   .3467485     0.80   0.424    -.4026838   
>  .9578005
              AMAMBAY  |   .0314709   .3838495     0.08   0.935     -.721555   
>  .7844968
            CANINDEYU  |          0  (omitted)
                 2102  |  -.2347739   .3569237    -0.66   0.511    -.9349774   
>  .4654296
                 2103  |   -.682115    .365115    -1.87   0.062    -1.398388   
>  .0341581
                 2106  |  -.1983372   .3188871    -0.62   0.534    -.8239216   
>  .4272473
                 2109  |   .1076165   .3411502     0.32   0.752    -.5616431   
>  .7768761
                 2113  |   -.111545   .2721936    -0.41   0.682    -.6455273   
>  .4224373
                 2114  |   -.247857   .3474475    -0.71   0.476    -.9294704   
>  .4337565
                 2117  |  -.0810741   .3755071    -0.22   0.829    -.8177341   
>  .6555859
                 2118  |   .1381478   .3711398     0.37   0.710    -.5899445   
>  .8662401
                 2120  |   .0043975   .5204577     0.01   0.993    -1.016623   
>  1.025418
                 2121  |  -.4705586   .2807063    -1.68   0.094    -1.021241   
>  .0801236
                 2122  |  -.2033739   .3842329    -0.53   0.597     -.957152   
>  .5504042
                 2125  |   .0001945   .2697802     0.00   0.999    -.5290531   
>  .5294422
                 2127  |          0  (omitted)
                       |
                  male |   .1212247   .0510217     2.38   0.018     .0211316   
>  .2213178
                   age |   .0000979   .0017774     0.06   0.956     -.003389   
>  .0035847
       householdincome |   .0034299   .0058987     0.58   0.561     -.008142   
>  .0150017
             education |   .0084472   .0069189     1.22   0.222    -.0051261   
>  .0220206
               working |  -.0145938   .0540005    -0.27   0.787    -.1205305   
>  .0913429
voted_lastpresidential |   .0666382   .0617716     1.08   0.281    -.0545437   
>  .1878201
        voteregistered |  -.1450009   .1056685    -1.37   0.170    -.3522986   
>  .0622968
               remesas |    .044854   .0606974     0.74   0.460    -.0742206   
>  .1639286
                 _cons |   2.660685   .3239962     8.21   0.000     2.025077   
>  3.296292
-------------------------------------------------------------------------------
---------

. local n5 = `e(N)'

. 
. //Panel D: 7 day bandwidth with Country FE, City Size FE, Province FE and ent
> ropy balancing weights
. eststo m_7: svy: reg trustusgov i.posttrump_7days i.country i.citysize i.prov
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(  51,   1324)   =       2.69
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0827

-------------------------------------------------------------------------------
------
                    |             Linearized
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.2628383   .0802179    -3.28   0.001    -.4202011   -.1
> 054755
                    |
            country |
          Honduras  |   .4355525   .3076579     1.42   0.157    -.1679776    1.
> 039083
          Paraguay  |   .0031595   .3403729     0.01   0.993    -.6645473    .6
> 708662
Dominican Republic  |   .6358411   .3672975     1.73   0.084    -.0846834    1.
> 356366
                    |
           citysize |
        Large City  |  -.1486565   .2115899    -0.70   0.482    -.5637308    .2
> 664179
       Medium City  |  -.0127074   .1798157    -0.07   0.944    -.3654505    .3
> 400357
        Small City  |   .0293722   .1845326     0.16   0.874    -.3326238    .3
> 913683
        Rural Area  |   .0476678    .179873     0.27   0.791    -.3051877    .4
> 005233
                    |
               prov |
               304  |   .4129698   .2890408     1.43   0.153    -.1540392    .9
> 799789
               305  |   .1455116   .3370752     0.43   0.666    -.5157262    .8
> 067495
               306  |   .0684856   .2955458     0.23   0.817    -.5112843    .6
> 482554
               307  |  -.0458536   .2703311    -0.17   0.865    -.5761599    .4
> 844526
               308  |   .0600275   .2756739     0.22   0.828    -.4807598    .6
> 008147
               309  |   .2435179   .3047075     0.80   0.424    -.3542243    .8
> 412601
               310  |   .1392116   .2725071     0.51   0.610    -.3953634    .6
> 737867
               401  |  -.5341726    .598673    -0.89   0.372    -1.708585    .6
> 402394
               404  |  -.1911825   .2829478    -0.68   0.499     -.746239    .3
> 638739
               405  |  -.0876991   .2315382    -0.38   0.705    -.5419058    .3
> 665075
               406  |  -.2752256   .2639355    -1.04   0.297    -.7929857    .2
> 425345
               407  |  -.3932279   .2540631    -1.55   0.122    -.8916214    .1
> 051656
               408  |  -.6192826   .3448242    -1.80   0.073    -1.295722    .0
> 571562
               409  |  -.2731421   .2492994    -1.10   0.273    -.7621907    .2
> 159064
               410  |  -.4530943   .3120741    -1.45   0.147    -1.065288     .
> 159099
               411  |   .2083333   .2748111     0.76   0.449    -.3307614    .7
> 474281
               412  |       -.25   .3740005    -0.67   0.504    -.9836738    .4
> 836738
               419  |  -.0367124   .2708095    -0.14   0.892    -.5679573    .4
> 945324
               420  |          0  (omitted)
        CONCEPCION  |   .1505326   .3410738     0.44   0.659    -.5185491    .8
> 196143
         SAN PEDRO  |   .0676865   .3003953     0.23   0.822    -.5215966    .6
> 569696
        CORDILLERA  |  -.1272793   .4563427    -0.28   0.780    -1.022483    .7
> 679245
            GUAIRA  |   .1288744   .3029594     0.43   0.671    -.4654387    .7
> 231875
          CAAGUAZU  |  -.2141993   .2837463    -0.75   0.450    -.7708221    .3
> 424234
           CAAZAPA  |   .4616659   .3490102     1.32   0.186    -.2229847    1.
> 146316
            ITAPUA  |  -.0646905   .2989039    -0.22   0.829    -.6510478    .5
> 216668
          MISIONES  |   .2090472   .2958667     0.71   0.480    -.3713522    .7
> 894466
         PARAGUARI  |  -.1735424   .3910254    -0.44   0.657    -.9406139     .
> 593529
       ALTO PARANA  |  -.1697444   .2754136    -0.62   0.538    -.7100211    .3
> 705323
           CENTRAL  |   .3041776   .2908282     1.05   0.296    -.2663378    .8
> 746929
          NEEMBUCU  |   .3111781   .2956955     1.05   0.293    -.2688853    .8
> 912416
           AMAMBAY  |   .0618531   .4145255     0.15   0.881    -.7513182    .8
> 750244
         CANINDEYU  |          0  (omitted)
              2102  |  -.1542959    .355525    -0.43   0.664    -.8517265    .5
> 431347
              2103  |  -.6759231   .4007436    -1.69   0.092    -1.462059    .1
> 102125
              2106  |  -.1915465   .3403606    -0.56   0.574    -.8592292    .4
> 761362
              2109  |   .0079636    .366819     0.02   0.983    -.7116222    .7
> 275494
              2113  |  -.1222932   .3123021    -0.39   0.695    -.7349338    .4
> 903475
              2114  |  -.2485354   .3370189    -0.74   0.461    -.9096627     .
> 412592
              2117  |   -.062971    .384806    -0.16   0.870    -.8178418    .6
> 918997
              2118  |    .079369    .380213     0.21   0.835    -.6664919    .8
> 252299
              2120  |   .0169338   .3941102     0.04   0.966     -.756189    .7
> 900567
              2121  |  -.4577103   .3218494    -1.42   0.155     -1.08908    .1
> 736591
              2122  |  -.2251998   .4094908    -0.55   0.582    -1.028495    .5
> 780951
              2125  |    .037751   .3089204     0.12   0.903    -.5682557    .6
> 437577
              2127  |          0  (omitted)
                    |
              _cons |   2.779618   .3048315     9.12   0.000     2.181632    3.
> 377603
-------------------------------------------------------------------------------
------

. eststo m_8: svy: reg trustusgov i.posttrump_7days##c.time_zero i.country i.ci
> tysize i.prov
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(  53,   1322)   =       2.60
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0834

-------------------------------------------------------------------------------
------
                    |             Linearized
         trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1793676    .124048    -1.45   0.148    -.4227115    .0
> 639763
          time_zero |  -.0069032      .0226    -0.31   0.760    -.0512375    .0
> 374311
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0178867   .0347167    -0.52   0.606    -.0859902    .0
> 502168
                    |
            country |
          Honduras  |   .5294155    .322121     1.64   0.101    -.1024866    1.
> 161318
          Paraguay  |   .0495833   .3433691     0.14   0.885    -.6240012    .7
> 231678
Dominican Republic  |   .6621487   .3686278     1.80   0.073    -.0609855    1.
> 385283
                    |
           citysize |
        Large City  |  -.1549067   .2135308    -0.73   0.468    -.5737884     .
> 263975
       Medium City  |  -.0226433   .1828351    -0.12   0.901    -.3813094    .3
> 360228
        Small City  |   .0175598   .1884345     0.09   0.926    -.3520906    .3
> 872102
        Rural Area  |   .0349041   .1831741     0.19   0.849    -.3244272    .3
> 942353
                    |
               prov |
               304  |    .364345   .2936778     1.24   0.215    -.2117603    .9
> 404504
               305  |   .1375685   .3427618     0.40   0.688    -.5348247    .8
> 099616
               306  |   .0745625   .2985216     0.25   0.803    -.5110451      
> .66017
               307  |  -.0268779   .2691662    -0.10   0.920    -.5548991    .5
> 011433
               308  |   .0513587   .2761923     0.19   0.853    -.4904456     .
> 593163
               309  |   .3182598   .3147886     1.01   0.312    -.2992584     .
> 935778
               310  |   .2399672   .2924058     0.82   0.412     -.333643    .8
> 135774
               401  |  -.6406814   .6108801    -1.05   0.294     -1.83904    .5
> 576772
               404  |  -.2802539   .2960457    -0.95   0.344    -.8610044    .3
> 004965
               405  |  -.1424033   .2392364    -0.60   0.552    -.6117115     .
> 326905
               406  |  -.3753861   .2856214    -1.31   0.189    -.9356873    .1
> 849151
               407  |  -.4866844   .2687714    -1.81   0.070    -1.013931    .0
> 405623
               408  |  -.6527808    .343632    -1.90   0.058    -1.326881    .0
> 213195
               409  |  -.3589737   .2633999    -1.36   0.173    -.8756832    .1
> 577359
               410  |  -.4674267   .3118676    -1.50   0.134    -1.079215    .1
> 443615
               411  |   .1265855   .2864368     0.44   0.659    -.4353152    .6
> 884863
               412  |  -.3792619   .3957428    -0.96   0.338    -1.155587    .3
> 970636
               419  |  -.1311763   .2869728    -0.46   0.648    -.6941285    .4
> 317759
               420  |          0  (omitted)
        CONCEPCION  |   .1236735   .3427864     0.36   0.718    -.5487679    .7
> 961149
         SAN PEDRO  |   .0061107   .3087148     0.02   0.984    -.5994927    .6
> 117141
        CORDILLERA  |  -.1869965   .4650097    -0.40   0.688    -1.099202    .7
> 252094
            GUAIRA  |   .0791349   .3073908     0.26   0.797    -.5238713     .
> 682141
          CAAGUAZU  |  -.2025026   .2831894    -0.72   0.475     -.758033    .3
> 530278
           CAAZAPA  |   .4137428    .353436     1.17   0.242    -.2795898    1.
> 107075
            ITAPUA  |  -.0905178   .2990068    -0.30   0.762     -.677077    .4
> 960414
          MISIONES  |   .1742982   .2975034     0.59   0.558    -.4093119    .7
> 579082
         PARAGUARI  |  -.2216334   .3950158    -0.56   0.575    -.9965328    .5
> 532659
       ALTO PARANA  |  -.1941677   .2756615    -0.70   0.481    -.7349306    .3
> 465952
           CENTRAL  |   .2565728   .2946725     0.87   0.384    -.3214839    .8
> 346294
          NEEMBUCU  |   .2795115   .2985367     0.94   0.349    -.3061255    .8
> 651485
           AMAMBAY  |   .0283256   .4157976     0.07   0.946    -.7873412    .8
> 439924
         CANINDEYU  |          0  (omitted)
              2102  |  -.1753675   .3567051    -0.49   0.623     -.875113     .
> 524378
              2103  |  -.7140023   .4089024    -1.75   0.081    -1.516143    .0
> 881383
              2106  |  -.2030181   .3414023    -0.59   0.552    -.8727442    .4
> 667081
              2109  |   .0775357   .3751259     0.21   0.836    -.6583458    .8
> 134173
              2113  |  -.1280832   .3127735    -0.41   0.682    -.7416484    .4
> 854821
              2114  |  -.2855397   .3459192    -0.83   0.409    -.9641266    .3
> 930473
              2117  |  -.0876253   .3887192    -0.23   0.822    -.8501726     .
> 674922
              2118  |   .1103087   .3825902     0.29   0.773    -.6402154    .8
> 608329
              2120  |  -.0220196   .4038143    -0.05   0.957     -.814179    .7
> 701397
              2121  |  -.4789982   .3238101    -1.48   0.139    -1.114214    .1
> 562175
              2122  |  -.2568182   .4142979    -0.62   0.535    -1.069543    .5
> 559066
              2125  |   .0019816   .3112277     0.01   0.995    -.6085512    .6
> 125145
              2127  |          0  (omitted)
                    |
              _cons |   2.756705    .307284     8.97   0.000     2.153908    3.
> 359502
-------------------------------------------------------------------------------
------

. local n7 = `e(N)'

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure A.6
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) || ///
>                         (m_7, msize(medsmall)) (m_8, msize(medsmall)), ///
>                         drop(*.country *.citysize *.prov male age householdin
> come  education working  voted_lastpresidential voteregistered remesas _cons)
>  xline(0, lpattern(solid)) byopts(row(2)) levels(95 90)       ///
>                         bylabels("A. Full sample, N=`n1' " "B. ± 7 days, N=`n
> 3' " "C. ± 7 days & Covariates, N=`n5' " "D. ± 7 days & Balancing, N=`n7' ") 
> subtitle(, size(small)) nokey      ///
>                         rename(1.posttrump = 1.posttrump_7days ///
>                         1.posttrump_14days = 1.posttrump_7days ///
>                         1.posttrump_21days = 1.posttrump_7days ///
>                         1.posttrump#c.time_zero = 1.posttrump_7days#c.time_ze
> ro ///
>                         1.posttrump_14days#c.time_zero = 1.posttrump_7days#c.
> time_zero ///
>                         1.posttrump_21days#c.time_zero = 1.posttrump_7days#c.
> time_zero) ///
>                         coeflabel(1.posttrump_7days = "Treatment"       ///
>                         1.posttrump_7days#c.time_zero = "Treatment*Days" _con
> s = "Constant") ///
>                         aspect(.4) mlabgap(*2)   

. 
. addplot 1: , b1title("", size(small)) norescaling

. addplot 2: , b1title("") norescaling

. addplot 3: , b1title("Effect on Trust in US Gov't") norescaling

. addplot 4: , b1title("Effect on Trust in US Gov't") norescaling

. 
. graph save Figure_MainResultsProvFE.gph, replace 
(file Figure_MainResultsProvFE.gph saved)

. graph export Figure_MainResultsProvFE.png, replace 
(file Figure_MainResultsProvFE.png written in PNG format)

. 
. drop sample_reg 

. 
.         
. *Table A.9. Ideology as an Additional Covariate
.         
. eststo clear

. eststo m_1: reg trustusgov i.posttrump_7days i.country i.citysize ideology ma
> le age householdincome education working  voted_lastpresidential voteregister
> ed remesas 

      Source |       SS           df       MS      Number of obs   =     1,258
-------------+----------------------------------   F(17, 1240)     =      5.18
       Model |  66.7067855        17  3.92392856   Prob > F        =    0.0000
    Residual |  938.596871     1,240  .756932961   R-squared       =    0.0664
-------------+----------------------------------   Adj R-squared   =    0.0536
       Total |  1005.30366     1,257  .799764246   Root MSE        =    .87002

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.2454562   .0526956    -4.66   0.000    -.3488385   
> -.1420739
                       |
               country |
             Honduras  |   .1469582   .0844001     1.74   0.082    -.0186246   
>   .312541
             Paraguay  |  -.0774839   .0864262    -0.90   0.370    -.2470416   
>  .0920739
   Dominican Republic  |   .3742658   .0862201     4.34   0.000     .2051123   
>  .5434193
                       |
              citysize |
           Large City  |  -.1221784   .1197075    -1.02   0.308    -.3570301   
>  .1126733
          Medium City  |  -.0200552   .1025273    -0.20   0.845    -.2212014   
>  .1810909
           Small City  |  -.0339402   .1140401    -0.30   0.766    -.2576731   
>  .1897926
           Rural Area  |   .0267301    .102509     0.26   0.794    -.1743802   
>  .2278403
                       |
              ideology |    .026246   .0082332     3.19   0.001     .0100934   
>  .0423986
                  male |   .1289438   .0527116     2.45   0.015       .02553   
>  .2323576
                   age |  -.0006172   .0018367    -0.34   0.737    -.0042205   
>  .0029861
       householdincome |   .0025562   .0059787     0.43   0.669    -.0091733   
>  .0142856
             education |   .0166465   .0071086     2.34   0.019     .0027003   
>  .0305927
               working |  -.0205051   .0547799    -0.37   0.708    -.1279766   
>  .0869663
voted_lastpresidential |    .067516   .0632758     1.07   0.286    -.0566234   
>  .1916555
        voteregistered |  -.1772756   .1091671    -1.62   0.105    -.3914484   
>  .0368971
               remesas |   .0520049   .0613464     0.85   0.397    -.0683493   
>  .1723591
                 _cons |   2.609166   .1679729    15.53   0.000     2.279624   
>  2.938709
-------------------------------------------------------------------------------
---------

. eststo m_2: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e ideology male age householdincome education working  voted_lastpresidential
>  voteregistered remesas 

      Source |       SS           df       MS      Number of obs   =     1,258
-------------+----------------------------------   F(19, 1238)     =      4.79
       Model |  68.8769405        19  3.62510213   Prob > F        =    0.0000
    Residual |  936.426716     1,238   .75640284   R-squared       =    0.0685
-------------+----------------------------------   Adj R-squared   =    0.0542
       Total |  1005.30366     1,257  .799764246   Root MSE        =    .86971

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1637915   .1113878    -1.47   0.142    -.3823212   
>  .0547381
             time_zero |   .0032757   .0192682     0.17   0.865    -.0345262   
>  .0410776
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |  -.0339019    .026302    -1.29   0.198    -.0855034   
>  .0176996
                       |
               country |
             Honduras  |   .1558291   .0845334     1.84   0.066    -.0100154   
>  .3216737
             Paraguay  |  -.0747939   .0865739    -0.86   0.388    -.2446417   
>  .0950538
   Dominican Republic  |    .369576   .0865634     4.27   0.000     .1997488   
>  .5394033
                       |
              citysize |
           Large City  |  -.1172041   .1197679    -0.98   0.328    -.3521747   
>  .1177665
          Medium City  |  -.0276838   .1026426    -0.27   0.787    -.2290564   
>  .1736888
           Small City  |  -.0379545   .1144022    -0.33   0.740     -.262398   
>  .1864891
           Rural Area  |   .0163741   .1027097     0.16   0.873    -.1851303   
>  .2178784
                       |
              ideology |   .0259535   .0082386     3.15   0.002     .0097904   
>  .0421166
                  male |   .1257444   .0527305     2.38   0.017     .0222934   
>  .2291955
                   age |   -.000503   .0018377    -0.27   0.784    -.0041084   
>  .0031024
       householdincome |   .0024412   .0059796     0.41   0.683      -.00929   
>  .0141725
             education |    .016887   .0071086     2.38   0.018     .0029408   
>  .0308331
               working |  -.0140908   .0548915    -0.26   0.797    -.1217814   
>  .0935998
voted_lastpresidential |   .0693519   .0632651     1.10   0.273    -.0547667   
>  .1934705
        voteregistered |   -.186586   .1092765    -1.71   0.088    -.4009736   
>  .0278015
               remesas |   .0574102   .0615707     0.93   0.351    -.0633844   
>  .1782047
                 _cons |   2.625477   .1832671    14.33   0.000     2.265929   
>  2.985026
-------------------------------------------------------------------------------
---------

. 
. coefplot (m_1, label(7-day window w/ Covariates)) ///
> (m_2, label(+ Interaction Term)) ///
> , keep(ideology) xline(0) xlabel(0(.01).05)

. 
.         graph export ideology_mainmodels.pdf, replace
(file /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroad/LAPOP
>  2016 original datasets/ideology_mainmodels.pdf written in PDF format)

.                 
. esttab using ideology.csv, nobaselevels b(3) se(3) starlevels(* 0.05 ** 0.01 
> *** 0.001 ) mtitles("7-day window w/ Covariates" "+ Interaction Term") consta
> nt label nogaps replace 
(output written to ideology.csv)

. 
. 
. *Table A.10. Heterogeneous Effects by Ideology  
. 
. eststo clear

. 
. eststo m_1: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered remesas 
> if ideology<6

      Source |       SS           df       MS      Number of obs   =       668
-------------+----------------------------------   F(16, 651)      =      4.24
       Model |  45.3278561        16    2.832991   Prob > F        =    0.0000
    Residual |   435.16915       651  .668462596   R-squared       =    0.0943
-------------+----------------------------------   Adj R-squared   =    0.0721
       Total |  480.497006       667  .720385316   Root MSE        =     .8176

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.3359983   .0675662    -4.97   0.000    -.4686724   
> -.2033242
                       |
               country |
             Honduras  |   .0163394   .1140168     0.14   0.886    -.2075458   
>  .2402245
             Paraguay  |  -.1611339   .1094142    -1.47   0.141    -.3759811   
>  .0537134
   Dominican Republic  |   .3471525   .1153434     3.01   0.003     .1206626   
>  .5736424
                       |
              citysize |
           Large City  |   .0790084    .151432     0.52   0.602    -.2183457   
>  .3763624
          Medium City  |    .083182   .1291387     0.64   0.520    -.1703967   
>  .3367607
           Small City  |   .1679553    .144089     1.17   0.244      -.11498   
>  .4508906
           Rural Area  |   .2040612   .1316612     1.55   0.122    -.0544706   
>   .462593
                       |
                  male |   .0228855    .068585     0.33   0.739    -.1117891   
>  .1575601
                   age |  -.0040505   .0024538    -1.65   0.099    -.0088688   
>  .0007679
       householdincome |   .0078132   .0077236     1.01   0.312     -.007353   
>  .0229794
             education |   .0052769   .0095046     0.56   0.579    -.0133865   
>  .0239403
               working |  -.1075973   .0713231    -1.51   0.132    -.2476483   
>  .0324538
voted_lastpresidential |   .1067137   .0793627     1.34   0.179     -.049124   
>  .2625514
        voteregistered |  -.1831191   .1366371    -1.34   0.181    -.4514218   
>  .0851836
               remesas |   .0363122   .0788436     0.46   0.645    -.1185062   
>  .1911306
                 _cons |   2.918929   .2173983    13.43   0.000     2.492043   
>  3.345815
-------------------------------------------------------------------------------
---------

. eststo m_2: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered remesas 
> if ideology>5

      Source |       SS           df       MS      Number of obs   =       706
-------------+----------------------------------   F(16, 689)      =      2.63
       Model |   35.972479        16  2.24827994   Prob > F        =    0.0005
    Residual |  588.646501       689  .854349058   R-squared       =    0.0576
-------------+----------------------------------   Adj R-squared   =    0.0357
       Total |   624.61898       705  .885984369   Root MSE        =    .92431

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1553317   .0752917    -2.06   0.039    -.3031605   
>  -.007503
                       |
               country |
             Honduras  |    .231028   .1136593     2.03   0.042     .0078678   
>  .4541882
             Paraguay  |   .0835283   .1244341     0.67   0.502    -.1607874   
>  .3278439
   Dominican Republic  |   .4174925    .121583     3.43   0.001     .1787749   
>  .6562102
                       |
              citysize |
           Large City  |  -.3328193    .176008    -1.89   0.059    -.6783956   
>  .0127571
          Medium City  |  -.0617616   .1516228    -0.41   0.684    -.3594597   
>  .2359365
           Small City  |  -.2225232   .1683411    -1.32   0.187    -.5530464   
>      .108
           Rural Area  |  -.0801602   .1466732    -0.55   0.585    -.3681402   
>  .2078198
                       |
                  male |   .2260507   .0739319     3.06   0.002     .0808919   
>  .3712095
                   age |   .0034135   .0025441     1.34   0.180    -.0015815   
>  .0084085
       householdincome |   -.002696   .0083706    -0.32   0.747     -.019131   
>   .013739
             education |   .0191039   .0097607     1.96   0.051    -.0000604   
>  .0382681
               working |   .0877056   .0766808     1.14   0.253    -.0628504   
>  .2382617
voted_lastpresidential |   .0602173   .0911444     0.66   0.509    -.1187368   
>  .2391714
        voteregistered |  -.1154237   .1546827    -0.75   0.456    -.4191299   
>  .1882824
               remesas |   .0698591   .0871405     0.80   0.423    -.1012338   
>  .2409519
                 _cons |   2.551784   .2204252    11.58   0.000     2.118998   
>   2.98457
-------------------------------------------------------------------------------
---------

. 
. coefplot (m_1, label(Liberal Respondents)) ///
> (m_2, label(Conservative Respondents)) ///
> , keep(posttrump_7days) xline(0)

. 
.         graph export ideology_HTE.pdf, replace
(file /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroad/LAPOP
>  2016 original datasets/ideology_HTE.pdf written in PDF format)

.                 
. esttab using ideology_HTE.csv, nobaselevels b(3) se(3) starlevels(* 0.05 ** 0
> .01  *** 0.001 ) mtitles("Liberal Respondents" "Conservative Respondents") co
> nstant label nogaps replace 
(output written to ideology_HTE.csv)

. 
. 
. *Table A.11. Intent to Emigrate as an Additional Covariate
. 
. eststo clear

. eststo m_1: reg trustusgov i.posttrump_7days i.country i.citysize emigrate re
> mesas male age householdincome education working  voted_lastpresidential vote
> registered 

      Source |       SS           df       MS      Number of obs   =     1,365
-------------+----------------------------------   F(17, 1347)     =      4.38
       Model |  58.0760621        17  3.41623895   Prob > F        =    0.0000
    Residual |  1051.63529     1,347  .780724048   R-squared       =    0.0523
-------------+----------------------------------   Adj R-squared   =    0.0404
       Total |  1109.71136     1,364  .813571375   Root MSE        =    .88359

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.2459509   .0511671    -4.81   0.000    -.3463267   
>  -.145575
                       |
               country |
             Honduras  |   .1430576   .0817222     1.75   0.080     -.017259   
>  .3033741
             Paraguay  |  -.0358791   .0836264    -0.43   0.668    -.1999312   
>   .128173
   Dominican Republic  |    .389524   .0849003     4.59   0.000     .2229728   
>  .5560751
                       |
              citysize |
           Large City  |  -.0927589   .1170524    -0.79   0.428    -.3223837   
>   .136866
          Medium City  |   .0280728   .0999621     0.28   0.779    -.1680256   
>  .2241711
           Small City  |   .0001353   .1109425     0.00   0.999    -.2175035   
>  .2177742
           Rural Area  |   .0929782   .0993644     0.94   0.350    -.1019475   
>   .287904
                       |
              emigrate |   .0113125   .0513907     0.22   0.826     -.089502   
>   .112127
               remesas |   .0476322   .0594676     0.80   0.423    -.0690269   
>  .1642914
                  male |   .1160227   .0509724     2.28   0.023     .0160288   
>  .2160167
                   age |   .0005271   .0018331     0.29   0.774    -.0030689   
>  .0041231
       householdincome |   .0028937   .0057592     0.50   0.615    -.0084043   
>  .0141917
             education |   .0109225   .0068465     1.60   0.111    -.0025084   
>  .0243535
               working |  -.0148787   .0529174    -0.28   0.779    -.1186881   
>  .0889307
voted_lastpresidential |   .0831082   .0610997     1.36   0.174    -.0367527   
>  .2029691
        voteregistered |  -.1241348   .1048406    -1.18   0.237    -.3298035   
>  .0815338
                 _cons |   2.663586   .1618625    16.46   0.000     2.346056   
>  2.981116
-------------------------------------------------------------------------------
---------

. eststo m_2: reg trustusgov i.posttrump_7days##c.time_zero i.country i.citysiz
> e emigrate remesas male age householdincome education working  voted_lastpres
> idential voteregistered 

      Source |       SS           df       MS      Number of obs   =     1,365
-------------+----------------------------------   F(19, 1345)     =      4.11
       Model |  60.8943843        19   3.2049676   Prob > F        =    0.0000
    Residual |  1048.81697     1,345   .77978957   R-squared       =    0.0549
-------------+----------------------------------   Adj R-squared   =    0.0415
       Total |  1109.71136     1,364  .813571375   Root MSE        =    .88306

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1605781   .1076594    -1.49   0.136    -.3717768   
>  .0506205
             time_zero |    .004395   .0187392     0.23   0.815    -.0323662   
>  .0411562
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |  -.0380682   .0257332    -1.48   0.139    -.0885497   
>  .0124134
                       |
               country |
             Honduras  |   .1540313   .0818843     1.88   0.060    -.0066036   
>  .3146662
             Paraguay  |   -.031126   .0837749    -0.37   0.710    -.1954697   
>  .1332178
   Dominican Republic  |   .3878021   .0852137     4.55   0.000      .220636   
>  .5549683
                       |
              citysize |
           Large City  |  -.0901416   .1171136    -0.77   0.442    -.3198869   
>  .1396037
          Medium City  |   .0173781   .1000735     0.17   0.862     -.178939   
>  .2136952
           Small City  |  -.0066755   .1112354    -0.06   0.952    -.2248892   
>  .2115382
           Rural Area  |    .077206   .0996679     0.77   0.439    -.1183155   
>  .2727274
                       |
              emigrate |   .0102247   .0513631     0.20   0.842    -.0905358   
>  .1109853
               remesas |   .0515855   .0596285     0.87   0.387    -.0653895   
>  .1685605
                  male |   .1129775    .050968     2.22   0.027     .0129922   
>  .2129629
                   age |   .0006222   .0018332     0.34   0.734     -.002974   
>  .0042184
       householdincome |   .0027683   .0057571     0.48   0.631    -.0085255   
>  .0140622
             education |   .0110359   .0068426     1.61   0.107    -.0023875   
>  .0244593
               working |  -.0080341   .0530097    -0.15   0.880    -.1120248   
>  .0959565
voted_lastpresidential |   .0832113   .0610632     1.36   0.173     -.036578   
>  .2030007
        voteregistered |  -.1289751   .1048227    -1.23   0.219    -.3346088   
>  .0766586
                 _cons |   2.683246   .1777552    15.10   0.000     2.334539   
>  3.031954
-------------------------------------------------------------------------------
---------

. 
. esttab using table1_emigrate.csv, nobaselevels b(3) se(3) starlevels(* 0.05 *
> * 0.01 *** 0.001 ) mtitles("7 Day Window w/Covariates" "+Interaction") consta
> nt label nogaps replace 
(output written to table1_emigrate.csv)

. 
. //In Appendix A we discuss how the emigration variable is well-balanced acros
> s our pre- and post-election respondents, and how in our sample remittance re
> cipients tend to express more trust in the US government. 
. //The code below reproduces those findings and others that we mention in the 
> text of Appendix A. 
. 
. ttest remesas, by(posttrump)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,346    .1921698    .0068125    .3940647    .1788127    .2055268
       1 |   2,788     .214132    .0077705    .4102924    .1988955    .2293684
---------+--------------------------------------------------------------------
combined |   6,134    .2021519    .0051282    .4016377    .1920989     .212205
---------+--------------------------------------------------------------------
    diff |           -.0219622    .0102961               -.0421461   -.0017783
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.1331
Ho: diff = 0                                     degrees of freedom =     6132

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0165         Pr(|T| > |t|) = 0.0330          Pr(T > t) = 0.9835

. ttest emigrate, by(posttrump)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,321    .3682626     .008371    .4824058    .3518497    .3846754
       1 |   2,770    .3592058    .0091174    .4798543    .3413282    .3770833
---------+--------------------------------------------------------------------
combined |   6,091    .3641438    .0061661    .4812288    .3520562    .3762315
---------+--------------------------------------------------------------------
    diff |            .0090568    .0123833               -.0152189    .0333325
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.7314
Ho: diff = 0                                     degrees of freedom =     6089

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7677         Pr(|T| > |t|) = 0.4646          Pr(T > t) = 0.2323

. 
. gen rem_emigrate=.
(6,157 missing values generated)

. replace rem_emigrate=0 if emigrate==0
(3,873 real changes made)

. replace rem_emigrate=0 if remesas==0
(1,653 real changes made)

. replace rem_emigrate=1 if remesas==1 & emigrate==1
(605 real changes made)

. 
. sum rem_emigrate

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
rem_emigrate |      6,131    .0986788    .2982547          0          1

. ttest rem_emigrate, by(posttrump)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,344    .0941986    .0050521    .2921485    .0842931    .1041041
       1 |   2,787    .1040545    .0057847    .3053861    .0927118    .1153973
---------+--------------------------------------------------------------------
combined |   6,131    .0986788    .0038091    .2982547    .0912117     .106146
---------+--------------------------------------------------------------------
    diff |            -.009856    .0076494               -.0248515    .0051396
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.2885
Ho: diff = 0                                     degrees of freedom =     6129

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0988         Pr(|T| > |t|) = 0.1976          Pr(T > t) = 0.9012

. 
. reg trustusgov remesas

      Source |       SS           df       MS      Number of obs   =     3,971
-------------+----------------------------------   F(1, 3969)      =      8.64
       Model |  7.36683913         1  7.36683913   Prob > F        =    0.0033
    Residual |  3385.64953     3,969  .853023313   R-squared       =    0.0022
-------------+----------------------------------   Adj R-squared   =    0.0019
       Total |  3393.01637     3,970  .854664073   Root MSE        =    .92359

------------------------------------------------------------------------------
  trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     remesas |   .1017047   .0346083     2.94   0.003     .0338529    .1695564
       _cons |   2.859586   .0167484   170.74   0.000     2.826749    2.892422
------------------------------------------------------------------------------

. reg trustusgov emigrate

      Source |       SS           df       MS      Number of obs   =     3,951
-------------+----------------------------------   F(1, 3949)      =      6.05
       Model |  5.17445853         1  5.17445853   Prob > F        =    0.0140
    Residual |  3378.50208     3,949  .855533573   R-squared       =    0.0015
-------------+----------------------------------   Adj R-squared   =    0.0013
       Total |  3383.67654     3,950  .856626972   Root MSE        =    .92495

------------------------------------------------------------------------------
  trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    emigrate |   .0736357   .0299416     2.46   0.014     .0149333    .1323381
       _cons |   2.853784   .0191251   149.22   0.000     2.816288     2.89128
------------------------------------------------------------------------------

. reg trustusgov rem_emigrate

      Source |       SS           df       MS      Number of obs   =     3,968
-------------+----------------------------------   F(1, 3966)      =      6.57
       Model |  5.60805673         1  5.60805673   Prob > F        =    0.0104
    Residual |   3387.3675     3,966  .854101739   R-squared       =    0.0017
-------------+----------------------------------   Adj R-squared   =    0.0014
       Total |  3392.97555     3,967  .855300115   Root MSE        =    .92418

------------------------------------------------------------------------------
  trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
rem_emigrate |     .11581   .0451954     2.56   0.010     .0272015    .2044184
       _cons |   2.869453   .0156371   183.50   0.000     2.838796    2.900111
------------------------------------------------------------------------------

. 
. ttest dummytrustus, by(remesas)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   3,041    .6721473     .008514     .469508    .6554535    .6888411
     Yes |     930    .7247312    .0146541      .44689    .6959722    .7534902
---------+--------------------------------------------------------------------
combined |   3,971    .6844624    .0073757    .4647882    .6700018    .6989229
---------+--------------------------------------------------------------------
    diff |           -.0525839    .0173985               -.0866946   -.0184731
------------------------------------------------------------------------------
    diff = mean(0) - mean(Yes)                                    t =  -3.0223
Ho: diff = 0                                     degrees of freedom =     3969

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0013         Pr(|T| > |t|) = 0.0025          Pr(T > t) = 0.9987

. ttest dummytrustus, by(emigrate)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |   2,339    .6733647    .0096992    .4690829    .6543448    .6923846
     Yes |   1,612    .7009926    .0114064    .4579652    .6786195    .7233656
---------+--------------------------------------------------------------------
combined |   3,951    .6846368    .0073933    .4647192    .6701418    .6991318
---------+--------------------------------------------------------------------
    diff |           -.0276279    .0150389               -.0571126    .0018569
------------------------------------------------------------------------------
    diff = mean(0) - mean(Yes)                                    t =  -1.8371
Ho: diff = 0                                     degrees of freedom =     3949

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0331         Pr(|T| > |t|) = 0.0663          Pr(T > t) = 0.9669

. 
. sum remesas if elsalv==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     remesas |      1,545    .2608414    .4392358          0          1

. sum remesas if dr==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     remesas |      1,511    .2263402    .4186004          0          1

. sum remesas if honduras==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     remesas |      1,558    .2516046    .4340745          0          1

. sum remesas if paraguay==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     remesas |      1,520    .0677632    .2514218          0          1

. 
. sum emigrate if elsalv==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    emigrate |      1,543    .3629294    .4810006          0          1

. sum emigrate if dr==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    emigrate |      1,501    .4183877    .4934589          0          1

. sum emigrate if honduras==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    emigrate |      1,544    .4060881    .4912605          0          1

. sum emigrate if paraguay==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    emigrate |      1,503    .2681304    .4431333          0          1

. 
. 
. *Table A.12. Heterogenous Effects by Ties to US, 7-day bandwidth
. 
. eststo clear

. 
. eststo m_1: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered if remes
> as==0 & emigrate==0

      Source |       SS           df       MS      Number of obs   =       636
-------------+----------------------------------   F(15, 620)      =      1.84
       Model |  21.3090005        15  1.42060004   Prob > F        =    0.0269
    Residual |  479.665842       620  .773654584   R-squared       =    0.0425
-------------+----------------------------------   Adj R-squared   =    0.0194
       Total |  500.974843       635   .78893676   Root MSE        =    .87958

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1385161   .0750927    -1.84   0.066     -.285983   
>  .0089508
                       |
               country |
             Honduras  |   .2357564   .1261875     1.87   0.062    -.0120504   
>  .4835632
             Paraguay  |   .0152126   .1185113     0.13   0.898    -.2175195   
>  .2479448
   Dominican Republic  |   .3524736   .1323258     2.66   0.008     .0926124   
>  .6123347
                       |
              citysize |
           Large City  |   -.152891   .1752026    -0.87   0.383    -.4969534   
>  .1911714
          Medium City  |  -.0763305   .1507237    -0.51   0.613    -.3723214   
>  .2196604
           Small City  |  -.0298361   .1652935    -0.18   0.857     -.354439   
>  .2947669
           Rural Area  |   -.005755   .1487367    -0.04   0.969    -.2978438   
>  .2863338
                       |
                  male |    .055953   .0768273     0.73   0.467    -.0949203   
>  .2068263
                   age |  -.0020653   .0024645    -0.84   0.402     -.006905   
>  .0027744
       householdincome |   .0011829   .0084417     0.14   0.889    -.0153948   
>  .0177606
             education |   .0100666   .0098577     1.02   0.308    -.0092919   
>   .029425
               working |   .1177217   .0790635     1.49   0.137     -.037543   
>  .2729865
voted_lastpresidential |   .1256539   .0911499     1.38   0.169     -.053346   
>  .3046539
        voteregistered |  -.1452241   .1619648    -0.90   0.370    -.4632902   
>   .172842
                 _cons |   2.747869   .2314798    11.87   0.000     2.293289   
>  3.202448
-------------------------------------------------------------------------------
---------

. eststo m_2: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered if remes
> as==1

      Source |       SS           df       MS      Number of obs   =       315
-------------+----------------------------------   F(15, 299)      =      1.70
       Model |  19.9501519        15  1.33001013   Prob > F        =    0.0495
    Residual |  233.560959       299  .781140332   R-squared       =    0.0787
-------------+----------------------------------   Adj R-squared   =    0.0325
       Total |  253.511111       314  .807360226   Root MSE        =    .88382

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.1944266   .1094132    -1.78   0.077    -.4097441   
>   .020891
                       |
               country |
             Honduras  |   .1831887   .1530865     1.20   0.232    -.1180748   
>  .4844522
             Paraguay  |  -.0352778   .2348727    -0.15   0.881    -.4974908   
>  .4269351
   Dominican Republic  |     .50499   .1691617     2.99   0.003     .1720917   
>  .8378883
                       |
              citysize |
           Large City  |  -.2749284   .2366891    -1.16   0.246    -.7407159   
>   .190859
          Medium City  |  -.1263978   .2065205    -0.61   0.541    -.5328155   
>  .2800199
           Small City  |  -.1447023   .2273324    -0.64   0.525    -.5920765   
>   .302672
           Rural Area  |   .0039911   .2021845     0.02   0.984    -.3938938   
>   .401876
                       |
                  male |   .2339313     .10664     2.19   0.029     .0240713   
>  .4437912
                   age |   .0012282   .0037855     0.32   0.746    -.0062214   
>  .0086777
       householdincome |    .007699   .0121736     0.63   0.528    -.0162578   
>  .0316557
             education |   .0075227   .0144179     0.52   0.602    -.0208508   
>  .0358962
               working |   -.237106    .112701    -2.10   0.036    -.4588937   
> -.0153183
voted_lastpresidential |   .0293495   .1196203     0.25   0.806    -.2060549   
>  .2647539
        voteregistered |  -.1421805   .2378014    -0.60   0.550     -.610157   
>  .3257959
                 _cons |   2.810485   .3564912     7.88   0.000     2.108936   
>  3.512035
-------------------------------------------------------------------------------
---------

. eststo m_3: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered if emigr
> ate==1

      Source |       SS           df       MS      Number of obs   =       578
-------------+----------------------------------   F(15, 562)      =      3.19
       Model |  37.9767179        15  2.53178119   Prob > F        =    0.0000
    Residual |    446.1565       562  .793872776   R-squared       =    0.0784
-------------+----------------------------------   Adj R-squared   =    0.0538
       Total |  484.133218       577  .839052371   Root MSE        =      .891

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.3437835   .0793127    -4.33   0.000    -.4995689   
>  -.187998
                       |
               country |
             Honduras  |   .0102872   .1290333     0.08   0.936    -.2431593   
>  .2637337
             Paraguay  |  -.1522194   .1402425    -1.09   0.278    -.4276829   
>  .1232441
   Dominican Republic  |   .3414355   .1289621     2.65   0.008     .0881289   
>   .594742
                       |
              citysize |
           Large City  |   .0964514   .1828087     0.53   0.598    -.2626203   
>  .4555231
          Medium City  |   .2250907   .1529002     1.47   0.142    -.0752349   
>  .5254164
           Small City  |   .1305548   .1784848     0.73   0.465     -.220024   
>  .4811337
           Rural Area  |   .3119974    .155538     2.01   0.045     .0064905   
>  .6175043
                       |
                  male |   .1252289   .0783616     1.60   0.111    -.0286884   
>  .2791463
                   age |   .0058845   .0034145     1.72   0.085    -.0008222   
>  .0125911
       householdincome |   .0050338   .0090773     0.55   0.579    -.0127957   
>  .0228633
             education |   .0147845   .0112131     1.32   0.188    -.0072403   
>  .0368092
               working |  -.1092712   .0815539    -1.34   0.181    -.2694589   
>  .0509165
voted_lastpresidential |   .0534357   .0938848     0.57   0.569    -.1309724   
>  .2378437
        voteregistered |  -.1042205   .1531484    -0.68   0.496    -.4050336   
>  .1965927
                 _cons |   2.447313   .2410589    10.15   0.000     1.973826   
>  2.920799
-------------------------------------------------------------------------------
---------

. eststo m_4: reg trustusgov posttrump_7days i.country i.citysize male age hous
> eholdincome education working  voted_lastpresidential voteregistered if rem_e
> migrate==1

      Source |       SS           df       MS      Number of obs   =       161
-------------+----------------------------------   F(15, 145)      =      0.76
       Model |  9.33803703        15  .622535802   Prob > F        =    0.7144
    Residual |  118.040845       145  .814074793   R-squared       =    0.0733
-------------+----------------------------------   Adj R-squared   =   -0.0226
       Total |  127.378882       160  .796118012   Root MSE        =    .90226

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       posttrump_7days |  -.0895633   .1580637    -0.57   0.572    -.4019698   
>  .2228431
                       |
               country |
             Honduras  |    .098411   .2478276     0.40   0.692    -.3914104   
>  .5882323
             Paraguay  |   .1346458   .4171034     0.32   0.747    -.6897422   
>  .9590338
   Dominican Republic  |   .3762579   .2635128     1.43   0.155    -.1445645   
>  .8970804
                       |
              citysize |
           Large City  |   .0167301   .3621348     0.05   0.963    -.6990147   
>  .7324749
          Medium City  |   .0604119   .3101926     0.19   0.846    -.5526711   
>   .673495
           Small City  |   .0684156   .4017113     0.17   0.865    -.7255505   
>  .8623816
           Rural Area  |   .2922579   .3157063     0.93   0.356    -.3317228   
>  .9162386
                       |
                  male |   .2345458   .1597926     1.47   0.144    -.0812778   
>  .5503694
                   age |   .0049658   .0074859     0.66   0.508    -.0098298   
>  .0197614
       householdincome |   .0104516   .0182501     0.57   0.568     -.025619   
>  .0465222
             education |    .015262   .0227166     0.67   0.503    -.0296364   
>  .0601603
               working |  -.3029754   .1645168    -1.84   0.068    -.6281362   
>  .0221854
voted_lastpresidential |   .0163933   .1676664     0.10   0.922    -.3149925   
>  .3477792
        voteregistered |   .1225515   .3177808     0.39   0.700    -.5055294   
>  .7506323
                 _cons |   2.168175   .5247522     4.13   0.000     1.131024   
>  3.205327
-------------------------------------------------------------------------------
---------

. 
. 
. coefplot (m_1, label(No Ties to US)) ///
> (m_2, label(Gets Remittances)) ///
> (m_3, label(Wants to Emigrate)) ///
> (m_4, label(Remittances & Emigrate)) ///
> , keep(posttrump) xline(0) xlabel(-.5(.1)0)
(m_1: no coefficients found, all dropped, or none kept)
(m_2: no coefficients found, all dropped, or none kept)
(m_3: no coefficients found, all dropped, or none kept)
(m_4: no coefficients found, all dropped, or none kept)
(nothing to plot)

. 
.         graph export USties_7day.pdf, replace
(file /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroad/LAPOP
>  2016 original datasets/USties_7day.pdf written in PDF format)

.                 
. esttab using USties_7day.csv, nobaselevels b(3) se(3) starlevels(* 0.05 ** 0.
> 01  *** 0.001 ) mtitles("No Ties to US" "Gets Remittances" "Wants to Emigrate
> " "Remittances & Emigrate") constant label nogaps replace 
(note: file USties_7day.csv not found)
(output written to USties_7day.csv)

. 
. 
. *Table A.13. Heterogenous Effects by Ties to US, Full Sample
. 
. eststo clear

. eststo m_1: reg trustusgov posttrump i.country i.citysize male age householdi
> ncome education working  voted_lastpresidential voteregistered if remesas==0 
> & emigrate==0

      Source |       SS           df       MS      Number of obs   =     1,549
-------------+----------------------------------   F(15, 1533)     =      5.16
       Model |  61.9642493        15  4.13094996   Prob > F        =    0.0000
    Residual |   1227.6497     1,533  .800815196   R-squared       =    0.0480
-------------+----------------------------------   Adj R-squared   =    0.0387
       Total |  1289.61394     1,548  .833083943   Root MSE        =    .89488

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
             posttrump |  -.2437377   .0510063    -4.78   0.000    -.3437872   
> -.1436882
                       |
               country |
             Honduras  |   .2258746   .0680096     3.32   0.001      .092473   
>  .3592763
             Paraguay  |  -.0479403   .0652783    -0.73   0.463    -.1759845   
>  .0801039
   Dominican Republic  |   .2305806   .0659072     3.50   0.000     .1013028   
>  .3598585
                       |
              citysize |
           Large City  |   .1360087    .082862     1.64   0.101    -.0265261   
>  .2985434
          Medium City  |   .0843553   .0749441     1.13   0.261    -.0626485   
>  .2313591
           Small City  |   .1473617    .084447     1.75   0.081    -.0182823   
>  .3130056
           Rural Area  |   .1625454   .0691898     2.35   0.019     .0268287   
>  .2982621
                       |
                  male |   .0088147   .0495749     0.18   0.859    -.0884272   
>  .1060565
                   age |  -.0003855   .0015682    -0.25   0.806    -.0034616   
>  .0026906
       householdincome |   .0113197   .0053954     2.10   0.036     .0007367   
>  .0219028
             education |   .0019776   .0062818     0.31   0.753    -.0103442   
>  .0142995
               working |   .0540964   .0510681     1.06   0.290    -.0460743   
>   .154267
voted_lastpresidential |   .0938816   .0589316     1.59   0.111    -.0217136   
>  .2094767
        voteregistered |  -.1203413   .1065655    -1.13   0.259    -.3293709   
>  .0886884
                 _cons |   2.674116   .1485364    18.00   0.000      2.38276   
>  2.965472
-------------------------------------------------------------------------------
---------

. eststo m_2: reg trustusgov posttrump i.country i.citysize male age householdi
> ncome education working  voted_lastpresidential voteregistered if remesas==1

      Source |       SS           df       MS      Number of obs   =       800
-------------+----------------------------------   F(15, 784)      =      4.16
       Model |  49.2631185        15   3.2842079   Prob > F        =    0.0000
    Residual |  618.485632       784  .788884734   R-squared       =    0.0738
-------------+----------------------------------   Adj R-squared   =    0.0561
       Total |   667.74875       799  .835730601   Root MSE        =    .88819

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
             posttrump |  -.3474052   .0719375    -4.83   0.000    -.4886182   
> -.2061922
                       |
               country |
             Honduras  |   .1117691   .0834174     1.34   0.181    -.0519789   
>  .2755171
             Paraguay  |  -.0765501   .1476747    -0.52   0.604    -.3664347   
>  .2133345
   Dominican Republic  |   .2644934   .0846761     3.12   0.002     .0982747   
>  .4307121
                       |
              citysize |
           Large City  |    .125279   .1076875     1.16   0.245    -.0861111   
>   .336669
          Medium City  |   .0898043   .1061778     0.85   0.398    -.1186222   
>  .2982307
           Small City  |   .1345838   .1219238     1.10   0.270     -.104752   
>  .3739195
           Rural Area  |   .3015429   .1049251     2.87   0.004     .0955754   
>  .5075104
                       |
                  male |   .1630088   .0673543     2.42   0.016     .0307927   
>  .2952249
                   age |  -.0007825   .0022532    -0.35   0.728    -.0052054   
>  .0036405
       householdincome |   .0069626   .0076456     0.91   0.363    -.0080457   
>  .0219709
             education |   .0135544   .0092586     1.46   0.144    -.0046202   
>   .031729
               working |  -.2398297   .0715629    -3.35   0.001    -.3803073   
> -.0993521
voted_lastpresidential |   .0569459   .0754336     0.75   0.451    -.0911298   
>  .2050215
        voteregistered |  -.0575109    .143534    -0.40   0.689    -.3392673   
>  .2242455
                 _cons |   2.714116   .2194413    12.37   0.000     2.283354   
>  3.144878
-------------------------------------------------------------------------------
---------

. eststo m_3: reg trustusgov posttrump i.country i.citysize male age householdi
> ncome education working  voted_lastpresidential voteregistered if emigrate==1

      Source |       SS           df       MS      Number of obs   =     1,381
-------------+----------------------------------   F(15, 1365)     =      5.94
       Model |  71.9338355        15  4.79558904   Prob > F        =    0.0000
    Residual |  1101.33047     1,365  .806835506   R-squared       =    0.0613
-------------+----------------------------------   Adj R-squared   =    0.0510
       Total |   1173.2643     1,380  .850191523   Root MSE        =    .89824

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
             posttrump |  -.3788002   .0545569    -6.94   0.000    -.4858246   
> -.2717757
                       |
               country |
             Honduras  |   .0611311   .0698951     0.87   0.382    -.0759823   
>  .1982446
             Paraguay  |  -.0965951   .0834957    -1.16   0.248    -.2603889   
>  .0671986
   Dominican Republic  |   .2734666   .0651965     4.19   0.000     .1455704   
>  .4013627
                       |
              citysize |
           Large City  |   .1735945   .0827934     2.10   0.036     .0111784   
>  .3360105
          Medium City  |   .1520125   .0754841     2.01   0.044      .003935   
>  .3000899
           Small City  |   .1714251   .0957575     1.79   0.074    -.0164227   
>  .3592729
           Rural Area  |   .2303353   .0742334     3.10   0.002     .0847113   
>  .3759593
                       |
                  male |   .1027737   .0510243     2.01   0.044     .0026792   
>  .2028682
                   age |   .0027439   .0021679     1.27   0.206    -.0015088   
>  .0069967
       householdincome |   .0025904   .0056069     0.46   0.644    -.0084088   
>  .0135896
             education |   .0147351   .0071539     2.06   0.040     .0007012   
>  .0287689
               working |  -.0782115   .0530402    -1.47   0.141    -.1822607   
>  .0258377
voted_lastpresidential |       .066   .0586519     1.13   0.261    -.0490576   
>  .1810576
        voteregistered |  -.0318669   .1006466    -0.32   0.752    -.2293056   
>  .1655717
                 _cons |   2.582972    .156208    16.54   0.000     2.276538   
>  2.889405
-------------------------------------------------------------------------------
---------

. eststo m_4: reg trustusgov posttrump i.country i.citysize male age householdi
> ncome education working  voted_lastpresidential voteregistered if rem_emigrat
> e==1

      Source |       SS           df       MS      Number of obs   =       403
-------------+----------------------------------   F(15, 387)      =      2.20
       Model |  25.6742921        15  1.71161947   Prob > F        =    0.0061
    Residual |   301.20412       387  .778305219   R-squared       =    0.0785
-------------+----------------------------------   Adj R-squared   =    0.0428
       Total |  326.878412       402  .813130378   Root MSE        =    .88222

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
             posttrump |  -.3470961    .105154    -3.30   0.001    -.5538408   
> -.1403514
                       |
               country |
             Honduras  |   .0370662    .123841     0.30   0.765    -.2064193   
>  .2805516
             Paraguay  |  -.0274375   .2356438    -0.12   0.907    -.4907397   
>  .4358647
   Dominican Republic  |   .1754523    .116174     1.51   0.132    -.0529588   
>  .4038635
                       |
              citysize |
           Large City  |   .1663946   .1524401     1.09   0.276    -.1333198   
>  .4661091
          Medium City  |   .1323693   .1512094     0.88   0.382    -.1649254   
>   .429664
           Small City  |   .1955261   .1869596     1.05   0.296    -.1720576   
>  .5631098
           Rural Area  |   .3898314   .1505597     2.59   0.010     .0938141   
>  .6858486
                       |
                  male |   .1717137   .0960781     1.79   0.075    -.0171867   
>  .3606142
                   age |   .0022694   .0042391     0.54   0.593    -.0060651   
>  .0106039
       householdincome |   .0160883   .0109052     1.48   0.141    -.0053524   
>  .0375291
             education |   .0126742   .0137325     0.92   0.357    -.0143254   
>  .0396738
               working |  -.2957929    .101462    -2.92   0.004    -.4952786   
> -.0963073
voted_lastpresidential |    .125404   .1039305     1.21   0.228    -.0789351   
>  .3297431
        voteregistered |  -.1243723   .1883268    -0.66   0.509     -.494644   
>  .2458993
                 _cons |   2.599997   .3112091     8.35   0.000     1.988125   
>  3.211869
-------------------------------------------------------------------------------
---------

.                 
. esttab using USties_fullsample.csv, nobaselevels b(3) se(3) starlevels(* 0.05
>  ** 0.01  *** 0.001 ) mtitles("No Ties to US" "Gets Remittances" "Wants to Em
> igrate" "Remittances & Emigrate") constant label nogaps replace 
(output written to USties_fullsample.csv)

. 
. *************************************
. *************************************
. //7. Appendix B: Tables and Figures//
. *************************************
. *************************************
.         
. *Table B.1. Descriptive Statistics Summarizing the Decline in Trust in the US
>  Gov’t
.         
. foreach var of varlist dummytrustusg{
  2.         
.         reg `var' posttrump_7days
  3.         global m`var'_0: di %6.3fc _b[_cons]
  4.         global m`var'_1: di %6.3fc _b[_cons] + _b[posttrump_7days]
  5.         global dif_`var': di %6.3fc _b[posttrump_7days]
  6. 
.         global lbe_`var' : var label `var'
  7. 
.         qui test posttrump_7days=0
  8.         global p_`var': di %12.3fc r(p)
  9.         glo star_`var'=cond(${p_`var'}<.001,"***",cond(${p_`var'}<.01,"**"
> ,cond(${p_`var'}<.05,"*",cond(${p_`var'}<.1,"+",""))))
 10. }

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(1, 1628)      =     18.68
       Model |  4.04543954         1  4.04543954   Prob > F        =    0.0000
    Residual |  352.568671     1,628  .216565523   R-squared       =    0.0113
-------------+----------------------------------   Adj R-squared   =    0.0107
       Total |   356.61411     1,629  .218915967   Root MSE        =    .46537

-------------------------------------------------------------------------------
--
  dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
posttrump_7days |  -.0996835    .023064    -4.32   0.000    -.1449219   -.05444
> 52
          _cons |       .725   .0160567    45.15   0.000     .6935061    .75649
> 39
-------------------------------------------------------------------------------
--

. 
. //Output the results of the balance test
.         texdoc init dummytrustusg.tex, replace force
(texdoc output file is dummytrustusg.tex)

.         tex \begin{tabular}{lccc} \toprule \toprule

.         tex Variable                    &       Mean Control    & Mean Treatm
> ent & Difference \\

.         tex \addlinespace \hline \\

.         foreach var of varlist dummytrustusg{
  2.         tex ${lbe_`var'} & ${m`var'_0} & ${m`var'_1} & ${dif_`var'}${star_
> `var'}\\
  3.         }

.         tex \hline \hline

.         tex \end{tabular}

. 
. 
. *Table B.2. Effect of Trump’s Election on Trust in the US Government
. 
. //These are the main results from Figure 2 in table form.
. //See code for Main Results in Figure 2 above to produce this table. 
.         
.         
. *Table B.3. Effect of Trump’s Election by Country
. 
. eststo clear

. * Panel A: Dominican Rep 7 day bandwidth with City Size FE plus covariate adj
> ustment
. eststo m_1: reg trustusgov i.posttrump_7days i.citysize male age householdinc
> ome education working  voted_lastpresidential voteregistered remesas if dr==1

      Source |       SS           df       MS      Number of obs   =       348
-------------+----------------------------------   F(12, 335)      =      1.05
       Model |  9.86899501        12  .822416251   Prob > F        =    0.4053
    Residual |  263.188476       335  .785637243   R-squared       =    0.0361
-------------+----------------------------------   Adj R-squared   =    0.0016
       Total |  273.057471       347  .786909139   Root MSE        =    .88636

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1819453   .1030016    -1.77   0.078    -.3845568   
>  .0206661
                       |
              citysize |
          Medium City  |   .1362215   .1285902     1.06   0.290    -.1167244   
>  .3891675
           Small City  |   .1655057   .1760448     0.94   0.348    -.1807869   
>  .5117983
           Rural Area  |  -.0388606   .1261763    -0.31   0.758    -.2870583   
>  .2093371
                       |
                  male |   .1457024   .1029075     1.42   0.158    -.0567239   
>  .3481287
                   age |   .0046896   .0036004     1.30   0.194    -.0023925   
>  .0117718
       householdincome |    .009246   .0111417     0.83   0.407    -.0126705   
>  .0311624
             education |   .0115296   .0135614     0.85   0.396    -.0151465   
>  .0382058
               working |  -.0985259   .1085079    -0.91   0.365    -.3119685   
>  .1149167
voted_lastpresidential |  -.0443621   .1433074    -0.31   0.757    -.3262578   
>  .2375336
        voteregistered |   .1044601   .2405223     0.43   0.664    -.3686643   
>  .5775846
               remesas |   .0795949   .1123902     0.71   0.479    -.1414846   
>  .3006743
                 _cons |    2.68034   .3106144     8.63   0.000      2.06934   
>  3.291341
-------------------------------------------------------------------------------
---------

. 
. * Panel B: Paraguay 7 day bandwidth with City Size FE plus covariate adjustme
> nt
. eststo m_2: reg trustusgov i.posttrump_7days i.citysize male age householdinc
> ome education working  voted_lastpresidential voteregistered remesas if parag
> uay==1

      Source |       SS           df       MS      Number of obs   =       328
-------------+----------------------------------   F(13, 314)      =      1.55
       Model |  14.9577932        13  1.15059948   Prob > F        =    0.0988
    Residual |  233.237329       314   .74279404   R-squared       =    0.0603
-------------+----------------------------------   Adj R-squared   =    0.0214
       Total |  248.195122       327  .759006489   Root MSE        =    .86185

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   -.345137   .1031338    -3.35   0.001    -.5480578   
> -.1422163
                       |
              citysize |
           Large City  |  -.0317528    .322096    -0.10   0.922     -.665492   
>  .6019864
          Medium City  |   .2831096   .2891677     0.98   0.328    -.2858416   
>  .8520609
           Small City  |   .2894432   .3043863     0.95   0.342    -.3094513   
>  .8883377
           Rural Area  |   .4280255   .2979888     1.44   0.152    -.1582817   
>  1.014333
                       |
                  male |   .0414398   .1059241     0.39   0.696     -.166971   
>  .2498506
                   age |  -.0032869    .003928    -0.84   0.403    -.0110154   
>  .0044416
       householdincome |   .0149584   .0117179     1.28   0.203    -.0080972   
>   .038014
             education |  -.0084232   .0148686    -0.57   0.571     -.037678   
>  .0208315
               working |  -.0101249   .1064413    -0.10   0.924    -.2195532   
>  .1993033
voted_lastpresidential |   .1362549    .132296     1.03   0.304    -.1240438   
>  .3965535
        voteregistered |  -.1219982    .164014    -0.74   0.458    -.4447035   
>  .2007071
               remesas |  -.0187818   .1837736    -0.10   0.919     -.380365   
>  .3428015
                 _cons |   2.661948   .3947385     6.74   0.000     1.885281   
>  3.438615
-------------------------------------------------------------------------------
---------

. 
. * Panel C: El Salvador 7 day bandwidth with City Size FE plus covariate adjus
> tment
. eststo m_3: reg trustusgov i.posttrump_7days i.citysize male age householdinc
> ome education working  voted_lastpresidential voteregistered remesas if elsal
> v==1

      Source |       SS           df       MS      Number of obs   =       365
-------------+----------------------------------   F(12, 352)      =      1.13
       Model |  10.3260285        12  .860502374   Prob > F        =    0.3339
    Residual |  267.947944       352   .76121575   R-squared       =    0.0371
-------------+----------------------------------   Adj R-squared   =    0.0043
       Total |  278.273973       364  .764488936   Root MSE        =    .87248

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1821616   .1040067    -1.75   0.081    -.3867144   
>  .0223911
                       |
              citysize |
          Medium City  |   .1006076   .1372162     0.73   0.464    -.1692592   
>  .3704744
           Small City  |  -.2381759   .1761278    -1.35   0.177    -.5845711   
>  .1082193
           Rural Area  |  -.0404484   .1406705    -0.29   0.774    -.3171088   
>  .2362119
                       |
                  male |     .10733   .0943533     1.14   0.256    -.0782371   
>   .292897
                   age |   .0004037   .0032457     0.12   0.901    -.0059797   
>  .0067872
       householdincome |  -.0095175   .0118888    -0.80   0.424    -.0328994   
>  .0138645
             education |   .0060919   .0140403     0.43   0.665    -.0215215   
>  .0337053
               working |   .0567868   .1014305     0.56   0.576    -.1426991   
>  .2562728
voted_lastpresidential |   .1200966    .118174     1.02   0.310    -.1123193   
>  .3525125
        voteregistered |  -.4685366   .3631496    -1.29   0.198    -1.182752   
>  .2456794
               remesas |   .1159416   .1075457     1.08   0.282    -.0955714   
>  .3274545
                 _cons |    3.11748   .4173767     7.47   0.000     2.296614   
>  3.938346
-------------------------------------------------------------------------------
---------

. 
. * Panel D: Honduras 7 day bandwidth with City Size FE plus covariate adjustme
> nt
. eststo m_4: reg trustusgov i.posttrump_7days i.citysize male age householdinc
> ome education working  voted_lastpresidential voteregistered remesas if hondu
> ras==1

      Source |       SS           df       MS      Number of obs   =       333
-------------+----------------------------------   F(12, 320)      =      1.32
       Model |  13.4389596        12   1.1199133   Prob > F        =    0.2061
    Residual |  271.756236       320  .849238236   R-squared       =    0.0471
-------------+----------------------------------   Adj R-squared   =    0.0114
       Total |  285.195195       332  .859021672   Root MSE        =    .92154

-------------------------------------------------------------------------------
---------
            trustusgov |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.2354994   .1117471    -2.11   0.036    -.4553512   
> -.0156476
                       |
              citysize |
          Medium City  |   .1348116   .1950051     0.69   0.490    -.2488424   
>  .5184655
           Small City  |   .1639977   .2272444     0.72   0.471     -.283084   
>  .6110794
           Rural Area  |   .3583271    .195953     1.83   0.068    -.0271917   
>  .7438459
                       |
                  male |    .171859   .1123057     1.53   0.127    -.0490918   
>  .3928099
                   age |  -.0023769   .0037458    -0.63   0.526    -.0097464   
>  .0049925
       householdincome |  -.0015687   .0125126    -0.13   0.900    -.0261861   
>  .0230487
             education |     .02774   .0137778     2.01   0.045     .0006334   
>  .0548466
               working |  -.0250804   .1191128    -0.21   0.833    -.2594236   
>  .2092628
voted_lastpresidential |   .1038138   .1136964     0.91   0.362     -.119873   
>  .3275007
        voteregistered |  -.1952409   .2118091    -0.92   0.357    -.6119552   
>  .2214734
               remesas |   .0416632   .1146748     0.36   0.717    -.1839487   
>   .267275
                 _cons |   2.655749   .2910253     9.13   0.000     2.083184   
>  3.228314
-------------------------------------------------------------------------------
---------

. 
. esttab using appendixb2.csv, nobaselevels b(3) se(3) starlevels(+ 0.1 * 0.05 
> ** 0.01 *** 0.001 ) mtitles("Dominican Republic" "Paraguay" "El Salvador" "Ho
> nduras") constant label nogaps replace 
(output written to appendixb2.csv)

. 
. esttab using appendixb2.tex, nobaselevels b(3) se(3) starlevels(+ 0.1 * 0.05 
> ** 0.01 *** 0.001 ) drop(*.citysize) mtitles("Dominican Republic" "Paraguay" 
> "El Salvador" "Honduras")  constant label nogaps replace 
(output written to appendixb2.tex)

. 
. 
. *Table B.4. Covariate Distribution
. 
. eststo clear

. estpost summarize elsalv honduras dr paraguay citysize male age householdinco
> me education working voted_lastpresidential voteregistered remesas if posttru
> mp==1, detail

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)
>   e(kurto~) 
-------------+-----------------------------------------------------------------
------------
      elsalv |      2799       2799   .3347624   .2227761   .4719917   .7002967
>    1.490416 
    honduras |      2799       2799   .1311183    .113967     .33759   2.185774
>    5.777608 
          dr |      2799       2799   .3133262   .2152298   .4639286   .8048961
>    1.647858 
    paraguay |      2799       2799   .2207931    .172105   .4148554   1.346285
>    2.812482 
    citysize |      2799       2799   3.554484    2.20995    1.48659  -.5354404
>    1.889519 
        male |      2799       2799   .5008932   .2500886   .5000885  -.0035727
>    1.000013 
         age |      2797       2797   39.53128   262.1168   16.19002   .6774615
>    2.666248 
householdi~e |      2333       2333   7.383626   25.95869   5.094967   .2250611
>    1.722963 
   education |      2749       2749    8.76355   20.01903   4.474264  -.0618166
>     2.33848 
     working |      2799       2799   .4233655   .2442144   .4941805   .3102033
>    1.096226 
voted_last~l |      2761       2761   .7189424   .2021374    .449597  -.9741261
>    1.948922 
voteregist~d |      2765       2765   .9269439   .0677434   .2602756    -3.2813
>    11.76693 
     remesas |      2788       2788    .214132   .1683399   .4102924   1.393734
>    2.942495 

             |    e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)
>      e(p25) 
-------------+-----------------------------------------------------------------
------------
      elsalv |       937          0          1          0          0          0
>           0 
    honduras |       367          0          1          0          0          0
>           0 
          dr |       877          0          1          0          0          0
>           0 
    paraguay |       618          0          1          0          0          0
>           0 
    citysize |      9949          1          5          1          1          1
>           3 
        male |      1402          0          1          0          0          0
>           0 
         age |    110569         18         93         18         19         21
>          26 
householdi~e |     17226          0         16          0          1          1
>           3 
   education |     24091          0         18          0          1          3
>           6 
     working |      1185          0          1          0          0          0
>           0 
voted_last~l |      1985          0          1          0          0          0
>           0 
voteregist~d |      2563          0          1          0          0          1
>           1 
     remesas |       597          0          1          0          0          0
>           0 

             |    e(p50)     e(p75)     e(p90)     e(p95)     e(p99) 
-------------+-------------------------------------------------------
      elsalv |         0          1          1          1          1 
    honduras |         0          0          1          1          1 
          dr |         0          1          1          1          1 
    paraguay |         0          0          1          1          1 
    citysize |         4          5          5          5          5 
        male |         1          1          1          1          1 
         age |        37         50         64         71         80 
householdi~e |         7         12         15         16         16 
   education |         9         12         15         16         18 
     working |         0          1          1          1          1 
voted_last~l |         1          1          1          1          1 
voteregist~d |         1          1          1          1          1 
     remesas |         0          0          1          1          1 

. esttab using appendixB31.csv, cells("mean Var skewness") replace
(output written to appendixB31.csv)

. 
. eststo clear

. estpost summarize elsalv honduras dr paraguay citysize male age householdinco
> me education working voted_lastpresidential voteregistered remesas if posttru
> mp==0, detail

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)
>   e(kurto~) 
-------------+-----------------------------------------------------------------
------------
      elsalv |      3358       3358   .1828469   .1494584   .3865986    1.64098
>    3.692816 
    honduras |      3358       3358    .355271   .2291217   .4786666   .6048074
>    1.365792 
          dr |      3358       3358   .1908874   .1544954   .3930591    1.57309
>    3.474612 
    paraguay |      3358       3358   .2709946   .1976154   .4445395   1.030457
>    2.061842 
    citysize |      3358       3358   2.931209   2.513288   1.585335   .0941615
>    1.475943 
        male |      3358       3358   .4952353   .2500518   .5000518   .0190598
>    1.000363 
         age |      3350       3350   39.53851   275.2304   16.59007   .6386174
>    2.485396 
householdi~e |      2845       2845   7.718453   26.35425   5.133639   .0985102
>    1.668623 
   education |      3236       3236   8.825093   19.52056   4.418207  -.0409268
>    2.392621 
     working |      3358       3358   .4264443   .2446624   .4946336   .2974591
>    1.088482 
voted_last~l |      3324       3324   .7006619   .2097979    .458037  -.8763136
>    1.767926 
voteregist~d |      3297       3297   .9099181    .081992   .2863425  -2.863566
>     9.20001 
     remesas |      3346       3346   .1921698   .1552869   .3940647   1.562567
>    3.441616 

             |    e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)
>      e(p25) 
-------------+-----------------------------------------------------------------
------------
      elsalv |       614          0          1          0          0          0
>           0 
    honduras |      1193          0          1          0          0          0
>           0 
          dr |       641          0          1          0          0          0
>           0 
    paraguay |       910          0          1          0          0          0
>           0 
    citysize |      9843          1          5          1          1          1
>           1 
        male |      1663          0          1          0          0          0
>           0 
         age |    132454         18        112         18         19         20
>          25 
householdi~e |     21959          0         16          0          0          1
>           3 
   education |     28558          0         18          0          1          3
>           6 
     working |      1432          0          1          0          0          0
>           0 
voted_last~l |      2329          0          1          0          0          0
>           0 
voteregist~d |      3000          0          1          0          0          1
>           1 
     remesas |       643          0          1          0          0          0
>           0 

             |    e(p50)     e(p75)     e(p90)     e(p95)     e(p99) 
-------------+-------------------------------------------------------
      elsalv |         0          0          1          1          1 
    honduras |         0          1          1          1          1 
          dr |         0          0          1          1          1 
    paraguay |         0          1          1          1          1 
    citysize |         3          5          5          5          5 
        male |         0          1          1          1          1 
         age |        36         51         64         70         80 
householdi~e |         8         12         15         16         16 
   education |         9         12         15         17         18 
     working |         0          1          1          1          1 
voted_last~l |         1          1          1          1          1 
voteregist~d |         1          1          1          1          1 
     remesas |         0          0          1          1          1 

. esttab using appendixB32.csv, cells("mean Var skewness") replace
(output written to appendixB32.csv)

. 
. 
. *Table B.5. Covariate Balance Tests, 7-Day Bandwidth Intervals*
. 
. //See code for Figure 1 above to produce this table.  
. 
. 
. 
. *Table B.6. Placebo Tests: Simulating a ‘Faux Election’
. 
. //See code for Figure 4 above to produce this table. 
. 
. 
. *Table B.7. Placebo Tests: Trust in Foreign Governments and Organizations
. 
. //See code for Figure 5 above to produce this table. 
. 
. 
. *Figure B.1. Power calculations with different bandwidths
. 
. // Generate matrix that includes the power and number of units in the treatme
> nt and control groups for two differnt effect sizes and multiple bandwidths
. 
. matrix results = J(24,5,.)

. matrix colnames results =  bandwidth n_contr n_treat  pow_eightsd pow_fifthsd

. local i 0 

. forval d = 0/23{
  2.         if `d' == 0{
  3.                 local d_neg = -1
  4.         }
  5.         if `d' != 0{
  6.                                 local d_neg = (`d'+1) * -1 
  7.         }
  8.         gen treatment_band = . 
  9.         replace treatment_band = 1 if time_zero >= 0 & time_zero <= `d' 
 10.         replace treatment_band = 0 if time_zero < 0 & time_zero >= `d_neg'
 11.         replace treatment_band = . if missing(male, age, householdincome, 
> citysize, education, working, voted_lastpresidential, voteregistered, remesas
> )                                                                 
 12.         local ++ i 
 13.         local bandwidth = `d' + 1 
 14.         qui: sum treatment_band if treatment_band == 1 
 15.         local n_treat = r(N)
 16.         qui: sum treatment_band if treatment_band == 0 
 17.         local n_contr = r(N)
 18.         sum trustusgov if treatment_naive == 0 
 19.         local sd = r(sd) 
 20.         local mean = r(mean)
 21.         local eight = `sd' / 8
 22.         local fifth = `sd' / 5
 23.         local eightsd = `mean' + `eight'
 24.         local fifthsd = `mean' + `fifth'
 25.         power twomeans `mean' `eightsd', sd(`sd') n1(`n_contr') n2(`n_trea
> t') 
 26.         local pow_fifthsd = r(power)
 27.         power twomeans `mean' `fifthsd', sd(`sd') n1(`n_contr') n2(`n_trea
> t')
 28.         local pow_eightsd = r(power)    
 29.         matrix results[`i', 1] = `bandwidth',`n_contr',`n_treat',`pow_fift
> hsd',`pow_eightsd'
 30.         drop treatment_band
 31. } 
(6,157 missing values generated)
(212 real changes made)
(149 real changes made)
(86 real changes made, 86 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       275
           N1 =       118
           N2 =       157
        N2/N1 =    1.3305
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.1757

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       275
           N1 =       118
           N2 =       157
        N2/N1 =    1.3305
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.3731
(6,157 missing values generated)
(383 real changes made)
(338 real changes made)
(143 real changes made, 143 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       578
           N1 =       278
           N2 =       300
        N2/N1 =    1.0791
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.3227

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       578
           N1 =       278
           N2 =       300
        N2/N1 =    1.0791
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.6695
(6,157 missing values generated)
(546 real changes made)
(526 real changes made)
(207 real changes made, 207 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       865
           N1 =       425
           N2 =       440
        N2/N1 =    1.0353
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.4507

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =       865
           N1 =       425
           N2 =       440
        N2/N1 =    1.0353
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.8358
(6,157 missing values generated)
(746 real changes made)
(777 real changes made)
(297 real changes made, 297 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,226
           N1 =       624
           N2 =       602
        N2/N1 =    0.9647
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.5896

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,226
           N1 =       624
           N2 =       602
        N2/N1 =    0.9647
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9380
(6,157 missing values generated)
(967 real changes made)
(1,034 real changes made)
(407 real changes made, 407 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,594
           N1 =       819
           N2 =       775
        N2/N1 =    0.9463
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.7029

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,594
           N1 =       819
           N2 =       775
        N2/N1 =    0.9463
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9788
(6,157 missing values generated)
(1,153 real changes made)
(1,221 real changes made)
(492 real changes made, 492 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,882
           N1 =       966
           N2 =       916
        N2/N1 =    0.9482
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.7731

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     1,882
           N1 =       966
           N2 =       916
        N2/N1 =    0.9482
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9912
(6,157 missing values generated)
(1,304 real changes made)
(1,355 real changes made)
(543 real changes made, 543 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,116
           N1 =     1,069
           N2 =     1,047
        N2/N1 =    0.9794
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.8195

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,116
           N1 =     1,069
           N2 =     1,047
        N2/N1 =    0.9794
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9958
(6,157 missing values generated)
(1,479 real changes made)
(1,551 real changes made)
(620 real changes made, 620 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,410
           N1 =     1,223
           N2 =     1,187
        N2/N1 =    0.9706
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.8658

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,410
           N1 =     1,223
           N2 =     1,187
        N2/N1 =    0.9706
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9984
(6,157 missing values generated)
(1,644 real changes made)
(1,708 real changes made)
(681 real changes made, 681 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,671
           N1 =     1,355
           N2 =     1,316
        N2/N1 =    0.9712
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.8977

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,671
           N1 =     1,355
           N2 =     1,316
        N2/N1 =    0.9712
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9993
(6,157 missing values generated)
(1,768 real changes made)
(1,851 real changes made)
(731 real changes made, 731 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,888
           N1 =     1,468
           N2 =     1,420
        N2/N1 =    0.9673
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9188

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     2,888
           N1 =     1,468
           N2 =     1,420
        N2/N1 =    0.9673
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9997
(6,157 missing values generated)
(1,862 real changes made)
(2,053 real changes made)
(794 real changes made, 794 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,121
           N1 =     1,625
           N2 =     1,496
        N2/N1 =    0.9206
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9367

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,121
           N1 =     1,625
           N2 =     1,496
        N2/N1 =    0.9206
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9999
(6,157 missing values generated)
(1,931 real changes made)
(2,241 real changes made)
(836 real changes made, 836 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,336
           N1 =     1,785
           N2 =     1,551
        N2/N1 =    0.8689
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9495

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,336
           N1 =     1,785
           N2 =     1,551
        N2/N1 =    0.8689
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    0.9999
(6,157 missing values generated)
(2,049 real changes made)
(2,420 real changes made)
(891 real changes made, 891 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,578
           N1 =     1,926
           N2 =     1,652
        N2/N1 =    0.8577
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9614

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,578
           N1 =     1,926
           N2 =     1,652
        N2/N1 =    0.8577
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,182 real changes made)
(2,555 real changes made)
(954 real changes made, 954 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,783
           N1 =     2,034
           N2 =     1,749
        N2/N1 =    0.8599
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9694

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,783
           N1 =     2,034
           N2 =     1,749
        N2/N1 =    0.8599
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,282 real changes made)
(2,643 real changes made)
(997 real changes made, 997 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,928
           N1 =     2,098
           N2 =     1,830
        N2/N1 =    0.8723
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9742

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     3,928
           N1 =     2,098
           N2 =     1,830
        N2/N1 =    0.8723
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,392 real changes made)
(2,768 real changes made)
(1,058 real changes made, 1,058 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,102
           N1 =     2,187
           N2 =     1,915
        N2/N1 =    0.8756
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9790

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,102
           N1 =     2,187
           N2 =     1,915
        N2/N1 =    0.8756
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,488 real changes made)
(2,881 real changes made)
(1,098 real changes made, 1,098 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,271
           N1 =     2,277
           N2 =     1,994
        N2/N1 =    0.8757
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9828

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,271
           N1 =     2,277
           N2 =     1,994
        N2/N1 =    0.8757
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,559 real changes made)
(2,999 real changes made)
(1,138 real changes made, 1,138 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,420
           N1 =     2,373
           N2 =     2,047
        N2/N1 =    0.8626
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9855

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,420
           N1 =     2,373
           N2 =     2,047
        N2/N1 =    0.8626
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,625 real changes made)
(3,078 real changes made)
(1,162 real changes made, 1,162 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,541
           N1 =     2,440
           N2 =     2,101
        N2/N1 =    0.8611
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9874

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,541
           N1 =     2,440
           N2 =     2,101
        N2/N1 =    0.8611
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,665 real changes made)
(3,146 real changes made)
(1,182 real changes made, 1,182 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,629
           N1 =     2,496
           N2 =     2,133
        N2/N1 =    0.8546
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9886

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,629
           N1 =     2,496
           N2 =     2,133
        N2/N1 =    0.8546
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,706 real changes made)
(3,214 real changes made)
(1,201 real changes made, 1,201 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,719
           N1 =     2,553
           N2 =     2,166
        N2/N1 =    0.8484
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9898

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,719
           N1 =     2,553
           N2 =     2,166
        N2/N1 =    0.8484
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,763 real changes made)
(3,215 real changes made)
(1,212 real changes made, 1,212 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,766
           N1 =     2,553
           N2 =     2,213
        N2/N1 =    0.8668
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9904

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,766
           N1 =     2,553
           N2 =     2,213
        N2/N1 =    0.8668
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,785 real changes made)
(3,248 real changes made)
(1,221 real changes made, 1,221 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,812
           N1 =     2,582
           N2 =     2,230
        N2/N1 =    0.8637
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9909

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,812
           N1 =     2,582
           N2 =     2,230
        N2/N1 =    0.8637
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000
(6,157 missing values generated)
(2,791 real changes made)
(3,283 real changes made)
(1,225 real changes made, 1,225 to missing)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
  trustusgov |      2,245    3.013363    .8764087          1          4

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,849
           N1 =     2,614
           N2 =     2,235
        N2/N1 =    0.8550
        delta =    0.1096
           m1 =    3.0134
           m2 =    3.1229
           sd =    0.8764

Estimated power:

        power =    0.9913

Estimated power for a two-sample means test
t test assuming sd1 = sd2 = sd
Ho: m2 = m1  versus  Ha: m2 != m1

Study parameters:

        alpha =    0.0500
            N =     4,849
           N1 =     2,614
           N2 =     2,235
        N2/N1 =    0.8550
        delta =    0.1753
           m1 =    3.0134
           m2 =    3.1886
           sd =    0.8764

Estimated power:

        power =    1.0000

. 
. //Transform matrix into variables
. svmat results, names(col)

. 
. //Total height of histogram bars
. gen tot_contr = n_treat + n_contr
(6,133 missing values generated)

. 
. //Generate Figure B.1
. 
. graph twoway    (bar tot_contr bandwidth, fcolor(gs4) ///
>                                 yaxis(2) ylabel(0(1000)6000, labsize(vsmall) 
> axis(2)) ytitle("Number of valid cases in treatment and control groups", axis
> (2) size(vsmall)) ///
>                                 yscale(axis(2) alt)) ///
>                                 (rbar tot_contr n_treat bandwidth, fcolor(gs1
> 3) lcolor(gs1)  lwidth(vthin) yaxis(2)) ///
>                                 (function y=.8, ra(0 24) lpattern(solid) lcol
> or(red) lwidth(vthin)) ///
>                                 (line pow_fifthsd bandwidth, lcolor(black) lp
> attern(solid) lcolor(black) yaxis(1) yscale(axis(1) alt)) ///
>                                 (line pow_eightsd bandwidth, lpattern(dash) l
> color(black) yaxis(1) ///
>                                 ytitle("Power", size(vsmall)) ylabel(0(.2)1, 
> labsize(vsmall) gmin gmax axis(1)) ///
>                                 xtitle("Bandwidth (± days) around Trump's ele
> ction", size(vsmall))  xlabel(0(3)24, labsize(vsmall)) ///
>                                 legend(ti("Effect size", size(vsmall)) order(
> 4 "1/5 standard deviation change" 5 "1/8 standard deviation change") size(vsm
> all) rows(1) pos(6)))

. 
. //Save Figure B.1 and drop extra variables
. 
. graph save powercalcs.gph, replace
(file powercalcs.gph saved)

. graph export powercalcs.png, replace
(file powercalcs.png written in PNG format)

. 
. drop bandwidth n_contr n_treat pow_fifthsd pow_eightsd tot_contr

. 
. 
. *Figure B.2. Effects of Trump’s election across all possible bandwidths
. 
. //Generate matrix that includes the effect estimates and the number of units 
> in the treatment and control groups for multiple bandwidths
. local i 0 

. matrix results = J(24,8,.)

. matrix colnames results = bandwidth coef ll95 ul95 ll90 ul90 n_treat n_contr

. forval d = 0/23{
  2.         if `d' == 0{
  3.                 local d_neg = -1
  4.         }
  5.         if `d' != 0{
  6.                 local d_neg = (`d'+1) * -1 
  7.         }
  8.         gen treatment_band = . 
  9.         replace treatment_band = 1 if time_zero >= 0 & time_zero <= `d'
 10.         replace treatment_band = 0 if time_zero < 0 & time_zero >= `d_neg'
 11.         replace treatment_band = . if fecha < 20767 & fecha >= 20767
 12.         local bandwidth = `d' + 1 
 13.         quietly: reg trustusgov i.treatment_band
 14.         local ++ i 
 15.         local coef = _b[1.treatment_band]
 16.         local degrees = e(df_r)
 17.         local critical_5 = invttail(`degrees', 0.025)
 18.         local se = _se[1.treatment_band]
 19.         local confvalue_5 = `critical_5' * `se'
 20.         local critical_10 = invttail(`degrees', 0.05)
 21.         local confvalue_10 = `critical_10' * `se'
 22.         local ll95 = `coef' - `confvalue_5'
 23.         local ul95 = `coef' + `confvalue_5'
 24.         local ll90 = `coef' - `confvalue_10'
 25.         local ul90 = `coef' + `confvalue_10'
 26.         qui: sum treatment_band if treatment_band == 1
 27.         local n_treat = r(N)
 28.         qui: sum treatment_band if treatment_band == 0 
 29.         local n_contr = r(N)
 30.         matrix results[`i', 1] = `bandwidth',`coef',`ll95',`ul95',`ll90',`
> ul90',`n_treat',`n_contr'
 31.         drop treatment_band
 32. } 
(6,157 missing values generated)
(212 real changes made)
(149 real changes made)
(0 real changes made)
(6,157 missing values generated)
(383 real changes made)
(338 real changes made)
(0 real changes made)
(6,157 missing values generated)
(546 real changes made)
(526 real changes made)
(0 real changes made)
(6,157 missing values generated)
(746 real changes made)
(777 real changes made)
(0 real changes made)
(6,157 missing values generated)
(967 real changes made)
(1,034 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,153 real changes made)
(1,221 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,304 real changes made)
(1,355 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,479 real changes made)
(1,551 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,644 real changes made)
(1,708 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,768 real changes made)
(1,851 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,862 real changes made)
(2,053 real changes made)
(0 real changes made)
(6,157 missing values generated)
(1,931 real changes made)
(2,241 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,049 real changes made)
(2,420 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,182 real changes made)
(2,555 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,282 real changes made)
(2,643 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,392 real changes made)
(2,768 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,488 real changes made)
(2,881 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,559 real changes made)
(2,999 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,625 real changes made)
(3,078 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,665 real changes made)
(3,146 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,706 real changes made)
(3,214 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,763 real changes made)
(3,215 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,785 real changes made)
(3,248 real changes made)
(0 real changes made)
(6,157 missing values generated)
(2,791 real changes made)
(3,283 real changes made)
(0 real changes made)

. 
. //Transform matrix into variables
. svmat results, names(col)

. 
. //Calculate total height of histogram bars
. gen tot_contr = n_treat + n_contr
(6,133 missing values generated)

. 
. //Generate Figure B.2
. 
. graph twoway    (bar tot_contr bandwidth, fcolor(gs4) ///
>                                 yaxis(2) ylabel(0(1000)6000, labsize(vsmall) 
> axis(2)) ytitle("Number of cases in treatment and control groups", axis(2) si
> ze(vsmall)) ///
>                                 yscale(axis(2) alt)) ///
>                                 (rbar tot_contr n_treat bandwidth, fcolor(gs1
> 3) lcolor(gs1)  lwidth(vthin) yaxis(2) legend(off)) ///
>                                 (function y=0, ra(0 24) lstyle(solid) lcolor(
> red) lwidth(vthin)) ///
>                                 (rspike ll95 ul95 bandwidth, lwidth(0.2) lcol
> or(black) yaxis(1) yscale(axis(1) alt)) ///
>                                 (rspike ll90 ul90 bandwidth, lwidth(0.4) lcol
> or(black) yaxis(1)) ///
>                                 (scatter coef bandwidth, msymbol(O) mcolor(bl
> ack) yaxis(1) ///
>                                 ytitle("Effects on Trust in US Gov't", size(v
> small)) ylabel(, labsize(vsmall) axis(1)) ///
>                                 xtitle("Bandwidth (± days) around election", 
> size(vsmall)) xlabel(0(3)24, labsize(vsmall)) legend(off))

. 
. graph save multiplebandwidths.gph, replace
(file multiplebandwidths.gph saved)

. graph export multiplebandwidths.png, replace
(file multiplebandwidths.png written in PNG format)

. 
. //Drop variables generated from matrix
. drop bandwidth coef ll95 ul95 ll90 ul90 n_treat n_contr tot_contr

. 
. 
. *Figure B.3. Covariate Balance Tests, 3-Day Bandwidth Intervals
. 
. //Create matrix for each 3-day bandwidth
. clear matrix

. foreach var of varlist treatment_naive  posttrump_21days posttrump_18days pos
> ttrump_15days posttrump_12days posttrump_9days posttrump_6days  posttrump_3da
> ys {
  2.         gen r_`var' = `var'
  3.         recode r_`var' (1=0) (0=1)
  4. }
(r_treatment_naive: 6157 changes made)
(237 missing values generated)
(r_posttrump_21days: 5920 changes made)
(599 missing values generated)
(r_posttrump_18days: 5558 changes made)
(1,232 missing values generated)
(r_posttrump_15days: 4925 changes made)
(1,985 missing values generated)
(r_posttrump_12days: 4172 changes made)
(2,805 missing values generated)
(r_posttrump_9days: 3352 changes made)
(3,783 missing values generated)
(r_posttrump_6days: 2374 changes made)
(5,085 missing values generated)
(r_posttrump_3days: 1072 changes made)

. 
. //Conduct t-test and store results in matrix to generate the plot
. foreach tr of varlist  treatment_naive  posttrump_21days posttrump_18days pos
> ttrump_15days posttrump_12days posttrump_9days posttrump_6days  posttrump_3da
> ys {
  2.         matrix mean = J(1,9,.)
  3.         matrix colnames mean =  male age householdincome citysize educatio
> n working voted_lastpresidential voteregistered remesas 
  4.         matrix CI = J(8,9,.)
  5.         matrix colnames CI =  male age householdincome citysize education 
> working  voted_lastpresidential voteregistered remesas 
  6.         matrix rownames CI = ll95 ul95 ll90 ul90
  7.         local i 0
  8.         foreach var of varlist  male age householdincome citysize educatio
> n working  voted_lastpresidential voteregistered remesas {
  9.                 quietly: ttest `var', by(`tr') 
 10.                 local ++ i 
 11.                 local diff =  r(mu_2) - r(mu_1) 
 12.                 matrix mean[1, `i'] = `diff' 
 13.                 local degrees = r(df_t)
 14.                 local critical_5 = invttail(`degrees', 0.025)
 15.                 local confvalue_5 = `critical_5' * r(se)
 16.                 local critical_10 = invttail(`degrees', 0.05)
 17.                 local confvalue_10 = `critical_10' * r(se)
 18.                 local ll95 = `diff' - `confvalue_5'
 19.                 local ul95 = `diff' + `confvalue_5'
 20.                 local ll90 = `diff' - `confvalue_10'
 21.                 local ul90 = `diff' + `confvalue_10'
 22.                 matrix CI[1, `i'] = `ll95' \ `ul95' \ `ll90' \ `ul90'
 23.         }
 24. matrix `tr'_m = mean
 25. matrix `tr'_CI = CI
 26. }

. 
. //Generate Figure B.3 from results stored in matrices 
. label var working "Working"

. label var voteregistered "Registered to Vote"

. label var voted_lastpresidential "Voted Last Presidential Election"

. label var remesas "Remittances"

. 
. coefplot (matrix(treatment_naive_m), xline(0) ci((treatment_naive_CI[1] treat
> ment_naive_CI[2]) (treatment_naive_CI[3] treatment_naive_CI[4]))) ///
>                 || (matrix(posttrump_21days_m), xline(0, lpattern(solid)) ci(
> (posttrump_21days_CI[1] posttrump_21days_CI[2]) (posttrump_21days_CI[3] postt
> rump_21days_CI[4]))) ///
>                 || (matrix(posttrump_18days_m), xline(0, lpattern(solid)) ci(
> (posttrump_18days_CI[1] posttrump_18days_CI[2]) (posttrump_18days_CI[3] postt
> rump_18days_CI[4]))) ///
>                 || (matrix(posttrump_15days_m), xline(0, lpattern(solid)) ci(
> (posttrump_15days_CI[1] posttrump_15days_CI[2]) (posttrump_15days_CI[3] postt
> rump_15days_CI[4]))) ///
>                 || (matrix(posttrump_12days_m), xline(0, lpattern(solid)) ci(
> (posttrump_12days_CI[1] posttrump_12days_CI[2]) (posttrump_12days_CI[3] postt
> rump_12days_CI[4]))) ///
>                 || (matrix(posttrump_9days_m), xline(0, lpattern(solid)) ci((
> posttrump_9days_CI[1] posttrump_9days_CI[2]) (posttrump_9days_CI[3] posttrump
> _9days_CI[4]))) ///
>                 || (matrix(posttrump_6days_m), xline(0, lpattern(solid)) ci((
> posttrump_6days_CI[1] posttrump_6days_CI[2]) (posttrump_6days_CI[3] posttrump
> _6days_CI[4]))) ///
>                 || (matrix(posttrump_3days_m), xline(0, lpattern(solid)) ci((
> posttrump_3days_CI[1] posttrump_3days_CI[2]) (posttrump_3days_CI[3] posttrump
> _3days_CI[4]))) ///
>                 , byopts(row(2)) xlabel(-1.5(1)1.5) ylabel(, labsize(small)) 
> xscale(range(-1 1.5)) xline(0, lpattern(solid))  ///
>                         nokey nooffset bylabels("Full sample" "± 21 days" "± 
> 18 days" "± 15 days" "± 12 days" "± 9 days" "± 6 days" "± 3 days") rescale(ma
> le remesas  voted_lastpresidential voteregistered =20 working =-10 citysize=2
>  ) xtitle("Mean Difference Between Treatment and Control Groups with 90% and 
> 95% Confidence Intervals")

. 
. //Save Figure B.3                       
. graph save balancetests_expanded.gph, replace 
(file balancetests_expanded.gph saved)

. graph export balancetests_expanded.png, replace 
(file balancetests_expanded.png written in PNG format)

. 
. 
. *Figure B.4. Mean Trust in US Government by Date (4-Point Scale) 
. 
. //A reviewer requested that we plot the mean DV by date. While the code below
>  generates that
. //figure, we caution against drawing substantive conclusions from it. 
. //The confidence intervals on the daily means are enormous, and the N varies 
> wildly from 
. //1 to 250+. Moreover, we know different countries have different levels of t
> rust in the 
. //US government, and a different number of people from each country were inte
> rviewed on each
. //day.
. 
. //We plot the mean DVs by date with 95% confidence intervals.
. 
. //Figure B.4 uses the 4-pt DV while Figure B.5 uses the binary DV.
. //Black circles are dates before the US election results were known, includin
> g election day.
. //Maroon squares are dates after the US election, starting with Nov. 9, 2016.
. 
. eststo clear

. 
. eststo m_1: mean trustusgov if fecha==20741

Mean estimation                   Number of obs   =         33

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.212121   .1287879      2.949789    3.474454
--------------------------------------------------------------

. eststo m_2: mean trustusgov if fecha==20742

Mean estimation                   Number of obs   =         33

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.121212   .1614188      2.792413    3.450011
--------------------------------------------------------------

. eststo m_3: mean trustusgov if fecha==20743

Mean estimation                   Number of obs   =         25

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |       3.04   .2039608      2.619046    3.460954
--------------------------------------------------------------

. eststo m_4: mean trustusgov if fecha==20744

Mean estimation                   Number of obs   =         31

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.967742   .1939065      2.571732    3.363752
--------------------------------------------------------------

. eststo m_5: mean trustusgov if fecha==20745

Mean estimation                   Number of obs   =          1

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |          3          .             .           .
--------------------------------------------------------------

. eststo m_6: mean trustusgov if fecha==20746

Mean estimation                   Number of obs   =         50

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |       3.08   .1335237      2.811674    3.348326
--------------------------------------------------------------

. eststo m_7: mean trustusgov if fecha==20747

Mean estimation                   Number of obs   =         51

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.882353   .1418286      2.597482    3.167224
--------------------------------------------------------------

. eststo m_8: mean trustusgov if fecha==20748

Mean estimation                   Number of obs   =         60

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.083333   .1171499      2.848917     3.31775
--------------------------------------------------------------

. eststo m_9: mean trustusgov if fecha==20749

Mean estimation                   Number of obs   =         81

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.024691    .086022      2.853502    3.195881
--------------------------------------------------------------

. eststo m_10: mean trustusgov if fecha==20750

Mean estimation                   Number of obs   =         79

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.063291   .0992372      2.865725    3.260857
--------------------------------------------------------------

. 
. eststo m_11: mean trustusgov if fecha==20751

Mean estimation                   Number of obs   =         91

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.032967   .0862324      2.861651    3.204283
--------------------------------------------------------------

. eststo m_12: mean trustusgov if fecha==20752

Mean estimation                   Number of obs   =         51

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.235294   .1069661      3.020446    3.450142
--------------------------------------------------------------

. eststo m_13: mean trustusgov if fecha==20753

Mean estimation                   Number of obs   =         95

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.136842   .0827989      2.972443    3.301241
--------------------------------------------------------------

. eststo m_14: mean trustusgov if fecha==20754

Mean estimation                   Number of obs   =        113

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.035398   .0897119      2.857646    3.213151
--------------------------------------------------------------

. eststo m_15: mean trustusgov if fecha==20755

Mean estimation                   Number of obs   =        130

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.115385    .076938      2.963161    3.267608
--------------------------------------------------------------

. eststo m_16: mean trustusgov if fecha==20756

Mean estimation                   Number of obs   =        140

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.985714   .0702263      2.846864    3.124564
--------------------------------------------------------------

. eststo m_17: mean trustusgov if fecha==20757

Mean estimation                   Number of obs   =        100

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |          3   .0887625      2.823876    3.176124
--------------------------------------------------------------

. eststo m_18: mean trustusgov if fecha==20758

Mean estimation                   Number of obs   =        104

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.057692    .081787      2.895487    3.219898
--------------------------------------------------------------

. eststo m_19: mean trustusgov if fecha==20759

Mean estimation                   Number of obs   =        137

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.043796    .074617      2.896236    3.191355
--------------------------------------------------------------

. eststo m_20: mean trustusgov if fecha==20760

Mean estimation                   Number of obs   =         89

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    3.05618    .096358      2.864688    3.247671
--------------------------------------------------------------

. 
. eststo m_21: mean trustusgov if fecha==20761

Mean estimation                   Number of obs   =        117

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.965812   .0831622      2.801099    3.130525
--------------------------------------------------------------

. eststo m_22: mean trustusgov if fecha==20762

Mean estimation                   Number of obs   =        153

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.895425       .076      2.745272    3.045578
--------------------------------------------------------------

. eststo m_23: mean trustusgov if fecha==20763

Mean estimation                   Number of obs   =        149

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.879195   .0705712      2.739737    3.018652
--------------------------------------------------------------

. eststo m_24: mean trustusgov if fecha==20764

Mean estimation                   Number of obs   =        117

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.008547   .0800601      2.849978    3.167116
--------------------------------------------------------------

. eststo m_25: mean trustusgov if fecha==20765

Mean estimation                   Number of obs   =        127

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    2.92126   .0729734      2.776848    3.065672
--------------------------------------------------------------

. eststo m_26: mean trustusgov if fecha==20766

Mean estimation                   Number of obs   =         88

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.931818   .0925595      2.747846     3.11579
--------------------------------------------------------------

. eststo m_27: mean trustusgov if fecha==20767

Mean estimation                   Number of obs   =        121

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    2.85124   .0827066      2.687486    3.014993
--------------------------------------------------------------

. eststo m_28: mean trustusgov if fecha==20768

Mean estimation                   Number of obs   =        105

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    2.92381   .0830896       2.75904    3.088579
--------------------------------------------------------------

. eststo m_29: mean trustusgov if fecha==20769

Mean estimation                   Number of obs   =        109

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.844037   .0923274      2.661028    3.027046
--------------------------------------------------------------

. eststo m_30: mean trustusgov if fecha==20770

Mean estimation                   Number of obs   =        123

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.658537   .0885481      2.483247    2.833826
--------------------------------------------------------------

. 
. eststo m_31: mean trustusgov if fecha==20771

Mean estimation                   Number of obs   =        129

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.674419   .0846524      2.506919    2.841918
--------------------------------------------------------------

. eststo m_32: mean trustusgov if fecha==20772

Mean estimation                   Number of obs   =        110

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.690909   .0883029      2.515896    2.865923
--------------------------------------------------------------

. eststo m_33: mean trustusgov if fecha==20773

Mean estimation                   Number of obs   =         93

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.688172   .0990588      2.491433    2.884911
--------------------------------------------------------------

. eststo m_34: mean trustusgov if fecha==20774

Mean estimation                   Number of obs   =        105

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |        2.6   .0981514      2.405362    2.794638
--------------------------------------------------------------

. eststo m_35: mean trustusgov if fecha==20775

Mean estimation                   Number of obs   =        106

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.632075    .096036      2.441654    2.822497
--------------------------------------------------------------

. eststo m_36: mean trustusgov if fecha==20776

Mean estimation                   Number of obs   =         77

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.545455   .1117582      2.322869     2.76804
--------------------------------------------------------------

. eststo m_37: mean trustusgov if fecha==20777

Mean estimation                   Number of obs   =         63

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    2.52381   .1277181      2.268505    2.779114
--------------------------------------------------------------

. eststo m_38: mean trustusgov if fecha==20778

Mean estimation                   Number of obs   =         42

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.452381   .1284337      2.193004    2.711758
--------------------------------------------------------------

. eststo m_39: mean trustusgov if fecha==20779

Mean estimation                   Number of obs   =         79

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.696203   .1055955      2.485978    2.906427
--------------------------------------------------------------

. eststo m_40: mean trustusgov if fecha==20780

Mean estimation                   Number of obs   =         79

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.721519   .1079999      2.506508     2.93653
--------------------------------------------------------------

. 
. eststo m_41: mean trustusgov if fecha==20781

Mean estimation                   Number of obs   =         60

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.883333   .1214127      2.640387     3.12628
--------------------------------------------------------------

. eststo m_42: mean trustusgov if fecha==20782

Mean estimation                   Number of obs   =         68

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.779412   .1085574       2.56273    2.996093
--------------------------------------------------------------

. eststo m_43: mean trustusgov if fecha==20783

Mean estimation                   Number of obs   =         64

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |    2.96875   .1112892      2.746356    3.191144
--------------------------------------------------------------

. eststo m_44: mean trustusgov if fecha==20784

Mean estimation                   Number of obs   =         43

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.604651   .1455867      2.310845    2.898457
--------------------------------------------------------------

. eststo m_45: mean trustusgov if fecha==20785

Mean estimation                   Number of obs   =         45

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.844444   .1454896      2.551229    3.137659
--------------------------------------------------------------

. eststo m_46: mean trustusgov if fecha==20786

Mean estimation                   Number of obs   =         25

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |       2.64    .237206      2.150431    3.129569
--------------------------------------------------------------

. eststo m_47: mean trustusgov if fecha==20787

Mean estimation                   Number of obs   =         26

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.730769   .2042449      2.310119    3.151419
--------------------------------------------------------------

. eststo m_48: mean trustusgov if fecha==20788

Mean estimation                   Number of obs   =         40

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |       2.75   .1591242      2.428141    3.071859
--------------------------------------------------------------

. eststo m_49: mean trustusgov if fecha==20789

Mean estimation                   Number of obs   =         18

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |        2.5   .2322102      2.010079    2.989921
--------------------------------------------------------------

. eststo m_50: mean trustusgov if fecha==20790

Mean estimation                   Number of obs   =          3

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   3.333333   .3333333      1.899116    4.767551
--------------------------------------------------------------

. 
. eststo m_51: mean trustusgov if fecha==20791

Mean estimation                   Number of obs   =          7

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
  trustusgov |   2.714286     .42056      1.685212    3.743359
--------------------------------------------------------------

. 
. coefplot (m_1, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_2, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_3, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_4, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_5, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_6, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_7, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_8, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_9, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_10, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_11, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_12, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_13, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_14, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_15, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_16, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_17, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_18, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_19, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_20, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_21, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_22, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_23, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_24, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_25, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_26, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_27, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_28, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_29, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_30, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_31, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_32, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_33, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_34, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_35, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_36, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_37, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_38, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_39, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_40, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_41, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_42, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_43, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_44, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_45, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_46, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_47, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_48, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_49, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_50, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_51, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
>         ,vertical aspect(.5) nokey xtitle("Date") ytitle("Mean Trust in the U
> S Gov't") title("") nolabel 
(m_5: could not determine CI1)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)

. 
.         
. graph save meantrustovertime.gph, replace 
(file meantrustovertime.gph saved)

. graph export meantrustovertime.png, replace
(file meantrustovertime.png written in PNG format)

. 
. 
. *Figure B.5. Proportion Trusting US Government by Date (Binary DV) 
. 
. eststo clear

. 
. eststo m_1: mean dummytrustusg if fecha==20741

Mean estimation                   Number of obs   =         33

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .8181818   .0681818         .6793    .9570636
---------------------------------------------------------------

. eststo m_2: mean dummytrustusg if fecha==20742

Mean estimation                   Number of obs   =         33

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7575758   .0757576      .6032626    .9118889
---------------------------------------------------------------

. eststo m_3: mean dummytrustusg if fecha==20743

Mean estimation                   Number of obs   =         25

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |        .76    .087178      .5800735    .9399265
---------------------------------------------------------------

. eststo m_4: mean dummytrustusg if fecha==20744

Mean estimation                   Number of obs   =         31

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6774194   .0853468      .5031179    .8517208
---------------------------------------------------------------

. eststo m_5: mean dummytrustusg if fecha==20745

Mean estimation                   Number of obs   =          1

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |          1          .             .           .
---------------------------------------------------------------

. eststo m_6: mean dummytrustusg if fecha==20746

Mean estimation                   Number of obs   =         50

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |        .72   .0641427      .5911004    .8488996
---------------------------------------------------------------

. eststo m_7: mean dummytrustusg if fecha==20747

Mean estimation                   Number of obs   =         51

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6666667   .0666667      .5327627    .8005706
---------------------------------------------------------------

. eststo m_8: mean dummytrustusg if fecha==20748

Mean estimation                   Number of obs   =         60

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7666667   .0550637      .6564844    .8768489
---------------------------------------------------------------

. eststo m_9: mean dummytrustusg if fecha==20749

Mean estimation                   Number of obs   =         81

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7901235   .0455286      .6995186    .8807283
---------------------------------------------------------------

. eststo m_10: mean dummytrustusg if fecha==20750

Mean estimation                   Number of obs   =         79

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7721519   .0474927      .6776012    .8667026
---------------------------------------------------------------

. eststo m_11: mean dummytrustusg if fecha==20751

Mean estimation                   Number of obs   =         91

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7692308   .0444116      .6809995    .8574621
---------------------------------------------------------------

. eststo m_12: mean dummytrustusg if fecha==20752

Mean estimation                   Number of obs   =         51

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .8823529   .0455645      .7908339     .973872
---------------------------------------------------------------

. eststo m_13: mean dummytrustusg if fecha==20753

Mean estimation                   Number of obs   =         95

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .8210526   .0395352      .7425545    .8995508
---------------------------------------------------------------

. eststo m_14: mean dummytrustusg if fecha==20754

Mean estimation                   Number of obs   =        113

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7610619   .0402943      .6812239    .8408999
---------------------------------------------------------------

. eststo m_15: mean dummytrustusg if fecha==20755

Mean estimation                   Number of obs   =        130

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7769231    .036654      .7044022    .8494439
---------------------------------------------------------------

. eststo m_16: mean dummytrustusg if fecha==20756

Mean estimation                   Number of obs   =        140

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7357143   .0374011      .6617657    .8096629
---------------------------------------------------------------

. eststo m_17: mean dummytrustusg if fecha==20757

Mean estimation                   Number of obs   =        100

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |        .71   .0456048      .6195102    .8004898
---------------------------------------------------------------

. eststo m_18: mean dummytrustusg if fecha==20758

Mean estimation                   Number of obs   =        104

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7788462   .0408935      .6977435    .8599488
---------------------------------------------------------------

. eststo m_19: mean dummytrustusg if fecha==20759

Mean estimation                   Number of obs   =        137

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7153285    .038695      .6388067    .7918503
---------------------------------------------------------------

. eststo m_20: mean dummytrustusg if fecha==20760

Mean estimation                   Number of obs   =         89

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7977528   .0428188      .7126595    .8828461
---------------------------------------------------------------

. eststo m_21: mean dummytrustusg if fecha==20761

Mean estimation                   Number of obs   =        117

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7179487   .0417813      .6351956    .8007018
---------------------------------------------------------------

. eststo m_22: mean dummytrustusg if fecha==20762

Mean estimation                   Number of obs   =        153

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6732026   .0380444      .5980386    .7483666
---------------------------------------------------------------

. eststo m_23: mean dummytrustusg if fecha==20763

Mean estimation                   Number of obs   =        149

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6979866   .0377403       .623407    .7725661
---------------------------------------------------------------

. eststo m_24: mean dummytrustusg if fecha==20764

Mean estimation                   Number of obs   =        117

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7350427   .0409746      .6538873    .8161982
---------------------------------------------------------------

. eststo m_25: mean dummytrustusg if fecha==20765

Mean estimation                   Number of obs   =        127

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7322835    .039445       .654223     .810344
---------------------------------------------------------------

. eststo m_26: mean dummytrustusg if fecha==20766

Mean estimation                   Number of obs   =         88

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7727273    .044929      .6834259    .8620286
---------------------------------------------------------------

. eststo m_27: mean dummytrustusg if fecha==20767

Mean estimation                   Number of obs   =        121

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .7024793   .0417335      .6198499    .7851087
---------------------------------------------------------------

. eststo m_28: mean dummytrustusg if fecha==20768

Mean estimation                   Number of obs   =        105

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6952381   .0451368      .6057302     .784746
---------------------------------------------------------------

. eststo m_29: mean dummytrustusg if fecha==20769

Mean estimation                   Number of obs   =        109

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6513761   .0458546      .5604844    .7422679
---------------------------------------------------------------

. eststo m_30: mean dummytrustusg if fecha==20770

Mean estimation                   Number of obs   =        123

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |    .601626    .044323      .5138843    .6893678
---------------------------------------------------------------

. eststo m_31: mean dummytrustusg if fecha==20771

Mean estimation                   Number of obs   =        129

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5736434   .0437122      .4871514    .6601354
---------------------------------------------------------------

. eststo m_32: mean dummytrustusg if fecha==20772

Mean estimation                   Number of obs   =        110

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5363636   .0477645       .441696    .6310313
---------------------------------------------------------------

. eststo m_33: mean dummytrustusg if fecha==20773

Mean estimation                   Number of obs   =         93

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6236559   .0505093      .5233401    .7239717
---------------------------------------------------------------

. eststo m_34: mean dummytrustusg if fecha==20774

Mean estimation                   Number of obs   =        105

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5619048   .0486518      .4654264    .6583831
---------------------------------------------------------------

. eststo m_35: mean dummytrustusg if fecha==20775

Mean estimation                   Number of obs   =        106

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5660377   .0483675      .4701338    .6619416
---------------------------------------------------------------

. eststo m_36: mean dummytrustusg if fecha==20776

Mean estimation                   Number of obs   =         77

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5324675   .0572329      .4184784    .6464567
---------------------------------------------------------------

. eststo m_37: mean dummytrustusg if fecha==20777

Mean estimation                   Number of obs   =         63

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5555556   .0631069      .4294067    .6817044
---------------------------------------------------------------

. eststo m_38: mean dummytrustusg if fecha==20778

Mean estimation                   Number of obs   =         42

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .3809524   .0758412      .2277879    .5341169
---------------------------------------------------------------

. eststo m_39: mean dummytrustusg if fecha==20779

Mean estimation                   Number of obs   =         79

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6455696   .0541614      .5377426    .7533966
---------------------------------------------------------------

. eststo m_40: mean dummytrustusg if fecha==20780

Mean estimation                   Number of obs   =         79

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5949367    .055584      .4842775    .7055959
---------------------------------------------------------------

. eststo m_41: mean dummytrustusg if fecha==20781

Mean estimation                   Number of obs   =         60

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6666667   .0613716      .5438623     .789471
---------------------------------------------------------------

. eststo m_42: mean dummytrustusg if fecha==20782

Mean estimation                   Number of obs   =         68

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6764706   .0571536      .5623914    .7905497
---------------------------------------------------------------

. eststo m_43: mean dummytrustusg if fecha==20783

Mean estimation                   Number of obs   =         64

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |     .71875   .0566454      .6055531    .8319469
---------------------------------------------------------------

. eststo m_44: mean dummytrustusg if fecha==20784

Mean estimation                   Number of obs   =         43

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5581395   .0766283      .4034973    .7127818
---------------------------------------------------------------

. eststo m_45: mean dummytrustusg if fecha==20785

Mean estimation                   Number of obs   =         45

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5777778   .0744603       .427713    .7278426
---------------------------------------------------------------

. eststo m_46: mean dummytrustusg if fecha==20786

Mean estimation                   Number of obs   =         25

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |        .56   .1013246      .3508764    .7691236
---------------------------------------------------------------

. eststo m_47: mean dummytrustusg if fecha==20787

Mean estimation                   Number of obs   =         26

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5384615   .0997037      .3331179    .7438052
---------------------------------------------------------------

. eststo m_48: mean dummytrustusg if fecha==20788

Mean estimation                   Number of obs   =         40

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |         .6   .0784465      .4413271    .7586729
---------------------------------------------------------------

. eststo m_49: mean dummytrustusg if fecha==20789

Mean estimation                   Number of obs   =         18

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .6111111   .1182356      .3616557    .8605665
---------------------------------------------------------------

. eststo m_50: mean dummytrustusg if fecha==20790

Mean estimation                   Number of obs   =          3

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |          1          0             .           .
---------------------------------------------------------------

. eststo m_51: mean dummytrustusg if fecha==20791

Mean estimation                   Number of obs   =          7

---------------------------------------------------------------
              |       Mean   Std. Err.     [95% Conf. Interval]
--------------+------------------------------------------------
dummytrustusg |   .5714286   .2020305      .0770777    1.065779
---------------------------------------------------------------

. 
. coefplot (m_1, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_2, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_3, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_4, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_5, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_6, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_7, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_8, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_9, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_10, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_11, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_12, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_13, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_14, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_15, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_16, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_17, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_18, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_19, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_20, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_21, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_22, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_23, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_24, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_25, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_26, mcolor(black) msymbol(0) ciopts(lcolor(black))) ///
> (m_27, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_28, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_29, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_30, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_31, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_32, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_33, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_34, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_35, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_36, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_37, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_38, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_39, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_40, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_41, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_42, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_43, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_44, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_45, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_46, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_47, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_48, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_49, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_50, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
> (m_51, mcolor(maroon) msymbol(S) ciopts(lcolor(maroon))) ///
>         ,vertical aspect(.5) nokey xtitle("Date") ytitle("Proportion Trusting
>  US Gov't") title("") nolabel 
(m_5: could not determine CI1)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)
(note:  named style 0 not found in class symbol, default attributes used)

.         
. graph save meantrustovertime_binary.gph, replace 
(file meantrustovertime_binary.gph saved)

. graph export meantrustovertime_binary.png, replace
(file meantrustovertime_binary.png written in PNG format)

.  
. 
. *Figure B.6 Main Results with Binary DV
. 
. eststo clear

. //Panel A: Full Sample with Country FE and City Size FE
. eststo m_1: reg dummytrustusg i.posttrump i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(8, 3976)      =     22.64
       Model |  37.4393325         8  4.67991657   Prob > F        =    0.0000
    Residual |  821.951885     3,976  .206728341   R-squared       =    0.0436
-------------+----------------------------------   Adj R-squared   =    0.0416
       Total |  859.391217     3,984  .215710647   Root MSE        =    .45467

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
        1.posttrump |  -.1485751   .0160488    -9.26   0.000    -.1800397   -.1
> 171105
                    |
            country |
          Honduras  |   .0564088   .0207516     2.72   0.007     .0157239    .0
> 970936
          Paraguay  |   .0155726   .0214611     0.73   0.468    -.0265032    .0
> 576484
Dominican Republic  |    .173576   .0202046     8.59   0.000     .1339638    .2
> 131883
                    |
           citysize |
        Large City  |    .025242   .0250935     1.01   0.315    -.0239553    .0
> 744393
       Medium City  |   .0235679   .0225859     1.04   0.297     -.020713    .0
> 678489
        Small City  |   .0393558   .0265774     1.48   0.139    -.0127509    .0
> 914625
        Rural Area  |   .0180511   .0204853     0.88   0.378    -.0221115    .0
> 582138
                    |
              _cons |   .6723893    .018816    35.74   0.000     .6354995    .7
> 092791
-------------------------------------------------------------------------------
------

. eststo m_2: reg dummytrustusg i.posttrump##c.time_zero i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     3,985
-------------+----------------------------------   F(10, 3974)     =     18.41
       Model |  38.0530154        10  3.80530154   Prob > F        =    0.0000
    Residual |  821.338202     3,974  .206677957   R-squared       =    0.0443
-------------+----------------------------------   Adj R-squared   =    0.0419
       Total |  859.391217     3,984  .215710647   Root MSE        =    .45462

-------------------------------------------------------------------------------
--------
        dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. I
> nterval]
----------------------+--------------------------------------------------------
--------
          1.posttrump |  -.1129584   .0266676    -4.24   0.000    -.1652419    
> -.060675
            time_zero |  -.0025135   .0017002    -1.48   0.139    -.0058468    
> .0008197
                      |
posttrump#c.time_zero |
                   1  |    .000844   .0025014     0.34   0.736    -.0040602    
> .0057481
                      |
              country |
            Honduras  |   .0410011   .0226229     1.81   0.070    -.0033525    
> .0853547
            Paraguay  |   .0113558   .0217761     0.52   0.602    -.0313376    
> .0540492
  Dominican Republic  |   .1704437   .0202987     8.40   0.000     .1306469    
> .2102404
                      |
             citysize |
          Large City  |   .0339823   .0258045     1.32   0.188    -.0166091    
> .0845736
         Medium City  |   .0328633   .0238239     1.38   0.168    -.0138449    
> .0795716
          Small City  |   .0479687   .0274485     1.75   0.081    -.0058458    
> .1017832
          Rural Area  |    .026777   .0217318     1.23   0.218    -.0158296    
> .0693836
                      |
                _cons |   .6473437    .026961    24.01   0.000     .5944851    
> .7002024
-------------------------------------------------------------------------------
--------

. local n1 = `e(N)'

. 
. //Panel B: 7 day bandwidth with Country FE and City Size FE
. eststo m_3: reg dummytrustusg i.posttrump_7days i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(8, 1621)      =      8.69
       Model |  14.6594818         8  1.83243522   Prob > F        =    0.0000
    Residual |  341.954629     1,621  .210952886   R-squared       =    0.0411
-------------+----------------------------------   Adj R-squared   =    0.0364
       Total |   356.61411     1,629  .218915967   Root MSE        =     .4593

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1233093   .0242311    -5.09   0.000     -.170837   -.0
> 757817
                    |
            country |
          Honduras  |   .0140154   .0389333     0.36   0.719    -.0623494    .0
> 903802
          Paraguay  |  -.0119557   .0381974    -0.31   0.754    -.0868771    .0
> 629657
Dominican Republic  |   .1938712   .0405212     4.78   0.000     .1143917    .2
> 733507
                    |
           citysize |
        Large City  |  -.0244382   .0559138    -0.44   0.662    -.1341091    .0
> 852328
       Medium City  |   .0246221   .0479621     0.51   0.608    -.0694521    .1
> 186964
        Small City  |   .0478545   .0527749     0.91   0.365    -.0556598    .1
> 513688
        Rural Area  |   .0307139   .0464173     0.66   0.508    -.0603302    .1
> 217581
                    |
              _cons |   .6689741   .0328688    20.35   0.000     .6045043     .
> 733444
-------------------------------------------------------------------------------
------

. eststo m_4: reg dummytrustusg i.posttrump_7days##c.time_zero i.country i.city
> size 

      Source |       SS           df       MS      Number of obs   =     1,630
-------------+----------------------------------   F(10, 1619)     =      7.67
       Model |  16.1239928        10  1.61239928   Prob > F        =    0.0000
    Residual |  340.490118     1,619  .210308905   R-squared       =    0.0452
-------------+----------------------------------   Adj R-squared   =    0.0393
       Total |   356.61411     1,629  .218915967   Root MSE        =    .45859

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1183755   .0511916    -2.31   0.021    -.2187844   -.0
> 179667
          time_zero |   .0126124   .0088159     1.43   0.153    -.0046795    .0
> 299043
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |  -.0311678   .0121172    -2.57   0.010    -.0549348   -.0
> 074007
                    |
            country |
          Honduras  |   .0223302   .0390026     0.57   0.567    -.0541707    .0
> 988311
          Paraguay  |  -.0116001   .0382711    -0.30   0.762    -.0866661    .0
> 634659
Dominican Republic  |   .1970695   .0406924     4.84   0.000     .1172542    .2
> 768849
                    |
           citysize |
        Large City  |  -.0260903   .0558827    -0.47   0.641    -.1357003    .0
> 835197
       Medium City  |   .0200236   .0479581     0.42   0.676    -.0740429    .1
> 140901
        Small City  |   .0477858   .0528435     0.90   0.366     -.055863    .1
> 514347
        Rural Area  |    .022711   .0465212     0.49   0.625     -.068537    .1
> 139591
                    |
              _cons |   .7204181   .0478246    15.06   0.000     .6266134    .8
> 142227
-------------------------------------------------------------------------------
------

. local n3 = `e(N)'

. 
. //Panel C: 7 day bandwidth with Country FE and City Size FE plus covariate ad
> justment
. eststo m_5: reg dummytrustusg i.posttrump_7days i.country i.citysize male age
>  householdincome education working  voted_lastpresidential voteregistered rem
> esas 

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(16, 1357)     =      5.21
       Model |  17.4147656        16  1.08842285   Prob > F        =    0.0000
    Residual |  283.462236     1,357  .208888899   R-squared       =    0.0579
-------------+----------------------------------   Adj R-squared   =    0.0468
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45704

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1433926    .026418    -5.43   0.000    -.1952171   
> -.0915681
                       |
               country |
             Honduras  |   .0677342   .0420086     1.61   0.107    -.0146746   
>  .1501429
             Paraguay  |  -.0017212   .0429052    -0.04   0.968    -.0858889   
>  .0824465
   Dominican Republic  |   .2144425   .0436529     4.91   0.000     .1288079   
>   .300077
                       |
              citysize |
           Large City  |  -.0764163   .0601724    -1.27   0.204    -.1944572   
>  .0416247
          Medium City  |   .0181522   .0515159     0.35   0.725    -.0829072   
>  .1192117
           Small City  |   .0203409   .0572631     0.36   0.722     -.091993   
>  .1326748
           Rural Area  |   .0591371   .0511634     1.16   0.248    -.0412309   
>  .1595051
                       |
                  male |   .0079392   .0262033     0.30   0.762    -.0434641   
>  .0593425
                   age |  -.0002035   .0009141    -0.22   0.824    -.0019966   
>  .0015896
       householdincome |  -.0014523    .002968    -0.49   0.625    -.0072746   
>    .00437
             education |   .0115795   .0035364     3.27   0.001     .0046422   
>  .0185168
               working |  -.0001602   .0273021    -0.01   0.995     -.053719   
>  .0533987
voted_lastpresidential |   .0186391   .0314642     0.59   0.554    -.0430847   
>  .0803629
        voteregistered |  -.0674139   .0537582    -1.25   0.210    -.1728721   
>  .0380442
               remesas |   .0424968   .0305868     1.39   0.165    -.0175058   
>  .1024994
                 _cons |   .6049438   .0804509     7.52   0.000     .4471222   
>  .7627655
-------------------------------------------------------------------------------
---------

. eststo m_6: reg dummytrustusg i.posttrump_7days##c.time_zero i.country i.city
> size male age householdincome education working  voted_lastpresidential voter
> egistered remesas 

      Source |       SS           df       MS      Number of obs   =     1,374
-------------+----------------------------------   F(18, 1355)     =      4.80
       Model |  18.0461777        18  1.00256543   Prob > F        =    0.0000
    Residual |  282.830824     1,355  .208731235   R-squared       =    0.0600
-------------+----------------------------------   Adj R-squared   =    0.0475
       Total |  300.877001     1,373  .219138384   Root MSE        =    .45687

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.1345479   .0555499    -2.42   0.016     -.243521   
> -.0255748
             time_zero |   .0082417   .0096825     0.85   0.395    -.0107527   
>  .0272361
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |  -.0220758   .0132617    -1.66   0.096    -.0480916   
>    .00394
                       |
               country |
             Honduras  |   .0721285   .0420699     1.71   0.087    -.0104006   
>  .1546576
             Paraguay  |  -.0019696   .0429839    -0.05   0.963    -.0862918   
>  .0823525
   Dominican Republic  |   .2157736   .0438421     4.92   0.000     .1297679   
>  .3017793
                       |
              citysize |
           Large City  |  -.0767094   .0602329    -1.27   0.203    -.1948693   
>  .0414504
          Medium City  |   .0145525    .051566     0.28   0.778    -.0866054   
>  .1157104
           Small City  |   .0206779   .0574128     0.36   0.719    -.0919498   
>  .1333056
           Rural Area  |   .0535704   .0512945     1.04   0.297    -.0470548   
>  .1541956
                       |
                  male |   .0065203   .0262063     0.25   0.804    -.0448891   
>  .0579297
                   age |  -.0001744   .0009144    -0.19   0.849    -.0019681   
>  .0016193
       householdincome |   -.001533   .0029675    -0.52   0.606    -.0073543   
>  .0042884
             education |   .0116316   .0035352     3.29   0.001     .0046966   
>  .0185665
               working |   .0025334   .0273476     0.09   0.926    -.0511149   
>  .0561816
voted_lastpresidential |   .0186318   .0314524     0.59   0.554    -.0430688   
>  .0803324
        voteregistered |  -.0693047   .0537655    -1.29   0.198    -.1747773   
>   .036168
               remesas |   .0459662   .0306818     1.50   0.134    -.0142229   
>  .1061552
                 _cons |   .6383097    .088987     7.17   0.000     .4637425   
>  .8128769
-------------------------------------------------------------------------------
---------

. local n5 = `e(N)'

. 
. //Panel D: 7 day bandwidth with Country FE, City Size FE, and entropy balanci
> ng weights
. eststo m_7: svy: reg dummytrustusg i.posttrump_7days i.country i.citysize
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(   8,   1367)   =       9.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0476

-------------------------------------------------------------------------------
------
                    |             Linearized
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1368891   .0261503    -5.23   0.000    -.1881879   -.0
> 855904
                    |
            country |
          Honduras  |   .0585806   .0444323     1.32   0.188    -.0285819    .1
> 457432
          Paraguay  |   -.000374   .0442661    -0.01   0.993    -.0872105    .0
> 864625
Dominican Republic  |   .2278196   .0435076     5.24   0.000     .1424711    .3
> 131681
                    |
           citysize |
        Large City  |  -.1015721   .0616578    -1.65   0.100    -.2225256    .0
> 193815
       Medium City  |  -.0034577   .0529807    -0.07   0.948    -.1073895    .1
> 004741
        Small City  |   .0014706   .0589448     0.02   0.980    -.1141608    .1
> 171021
        Rural Area  |   .0212552   .0520017     0.41   0.683     -.080756    .1
> 232664
                    |
              _cons |   .6794087    .035869    18.94   0.000     .6090447    .7
> 497727
-------------------------------------------------------------------------------
------

. eststo m_8: svy: reg dummytrustusg i.posttrump_7days##c.time_zero i.country i
> .citysize  
(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         1                  Number of obs     =      1,375
Number of PSUs     =     1,375                  Population size   = 1,367.3577
                                                Design df         =      1,374
                                                F(  10,   1365)   =       8.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0494

-------------------------------------------------------------------------------
------
                    |             Linearized
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.1247878   .0542717    -2.30   0.022    -.2312521   -.0
> 183234
          time_zero |   .0069092   .0090164     0.77   0.444    -.0107781    .0
> 245965
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |   -.019992   .0129502    -1.54   0.123    -.0453963    .0
> 054122
                    |
            country |
          Honduras  |   .0630208   .0445466     1.41   0.157    -.0243658    .1
> 504074
          Paraguay  |  -.0003356   .0442962    -0.01   0.994    -.0872312    .0
> 865599
Dominican Republic  |   .2287797    .043573     5.25   0.000     .1433029    .3
> 142566
                    |
           citysize |
        Large City  |  -.1008634   .0617676    -1.63   0.103    -.2220324    .0
> 203056
       Medium City  |  -.0066988   .0530315    -0.13   0.899    -.1107302    .0
> 973326
        Small City  |   .0016583     .05905     0.03   0.978    -.1141797    .1
> 174964
        Rural Area  |   .0156965   .0521953     0.30   0.764    -.0866946    .1
> 180877
                    |
              _cons |   .7076798   .0496893    14.24   0.000     .6102048    .8
> 051549
-------------------------------------------------------------------------------
------

. local n7 = `e(N)'

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure B.6
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) || ///
>                         (m_7, msize(medsmall)) (m_8, msize(medsmall)), ///
>                         drop(*.country *.citysize male age householdincome  e
> ducation working  voted_lastpresidential voteregistered remesas _cons) xline(
> 0, lpattern(solid)) byopts(row(2)) levels(95 90)      ///
>                         bylabels("A. Full sample, N=`n1' " "B. ± 7 days, N=`n
> 3' " "C. ± 7 days & Covariates, N=`n5' " "D. ± 7 days & Balancing, N=`n7' ") 
> subtitle(, size(small)) nokey      ///
>                         rename(1.posttrump = 1.posttrump_7days ///
>                         1.posttrump_14days = 1.posttrump_7days ///
>                         1.posttrump_21days = 1.posttrump_7days ///
>                         1.posttrump#c.time_zero = 1.posttrump_7days#c.time_ze
> ro ///
>                         1.posttrump_14days#c.time_zero = 1.posttrump_7days#c.
> time_zero ///
>                         1.posttrump_21days#c.time_zero = 1.posttrump_7days#c.
> time_zero) ///
>                         coeflabel(1.posttrump_7days = "Treatment"       ///
>                         1.posttrump_7days#c.time_zero = "Treatment*Days" _con
> s = "Constant") ///
>                         aspect(.4) mlabgap(*2)   

. 
. addplot 1: , b1title("", size(small)) norescaling

. addplot 2: , b1title("") norescaling

. addplot 3: , b1title("Effect on Trust in US Gov't") norescaling

. addplot 4: , b1title("Effect on Trust in US Gov't") norescaling

. 
. //Save Figure B.6
. 
. graph save Figure_1_binary.gph, replace 
(file Figure_1_binary.gph saved)

. graph export Figure_1_binary.png, replace 
(file Figure_1_binary.png written in PNG format)

. 
. drop sample_reg 

. 
. 
. * Figure B.7 Placebo Test (Faux Election) with Binary DV
. 
. eststo clear

. 
. //Fictional Election Placebo Test, Naive
. 
. eststo m_1: reg dummytrustusg i.placebo_7days

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(1, 1713)      =      0.60
       Model |  .112701708         1  .112701708   Prob > F        =    0.4389
    Residual |  322.073887     1,713  .188017447   R-squared       =    0.0003
-------------+----------------------------------   Adj R-squared   =   -0.0002
       Total |  322.186589     1,714  .187973506   Root MSE        =    .43361

-------------------------------------------------------------------------------
--
  dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interva
> l]
----------------+--------------------------------------------------------------
--
1.placebo_7days |  -.0162696   .0210142    -0.77   0.439    -.0574858    .02494
> 65
          _cons |   .7567277   .0142263    53.19   0.000      .728825    .78463
> 04
-------------------------------------------------------------------------------
--

. eststo m_2: reg dummytrustusg i.placebo_7days##c.placebo_time_zero

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(3, 1711)      =      1.39
       Model |  .785955379         3  .261985126   Prob > F        =    0.2427
    Residual |  321.400634     1,711  .187843737   R-squared       =    0.0024
-------------+----------------------------------   Adj R-squared   =    0.0007
       Total |  322.186589     1,714  .187973506   Root MSE        =    .43341

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0215901   .0432506    -0.50   0.618    -.1064196    .0
> 632394
  placebo_time_zero |  -.0078915   .0068818    -1.15   0.252    -.0213891    .0
> 056062
                    |
      placebo_7days#|
c.placebo_time_zero |
                 1  |   .0208893   .0110368     1.89   0.059    -.0007578    .0
> 425363
                    |
              _cons |   .7242784   .0316695    22.87   0.000     .6621634    .7
> 863934
-------------------------------------------------------------------------------
------

. 
. //Fictional Election Placebo Test, Fixed Effects Only
. 
. eststo m_3: reg dummytrustusg i.placebo_7days i.country i.citysize

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(8, 1706)      =      4.82
       Model |  7.11914201         8  .889892751   Prob > F        =    0.0000
    Residual |  315.067447     1,706  .184681974   R-squared       =    0.0221
-------------+----------------------------------   Adj R-squared   =    0.0175
       Total |  322.186589     1,714  .187973506   Root MSE        =    .42975

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0304603   .0215113    -1.42   0.157    -.0726515     .
> 011731
                    |
            country |
          Honduras  |   .0900537   .0321713     2.80   0.005     .0269543    .1
> 531531
          Paraguay  |   .0285143   .0332999     0.86   0.392    -.0367985    .0
> 938272
Dominican Republic  |   .1642053   .0301373     5.45   0.000     .1050952    .2
> 233153
                    |
           citysize |
        Large City  |   .0264197   .0359318     0.74   0.462    -.0440554    .0
> 968947
       Medium City  |   .0403901   .0365731     1.10   0.270    -.0313429     .
> 112123
        Small City  |   .0702709   .0397705     1.77   0.077    -.0077332     .
> 148275
        Rural Area  |   .0359217   .0346538     1.04   0.300    -.0320467    .1
> 038902
                    |
              _cons |   .6661939   .0243978    27.31   0.000     .6183411    .7
> 140466
-------------------------------------------------------------------------------
------

. eststo m_4: reg dummytrustusg i.placebo_7days##c.placebo_time_zero i.country 
> i.citysize

      Source |       SS           df       MS      Number of obs   =     1,715
-------------+----------------------------------   F(10, 1704)     =      3.95
       Model |  7.29086755        10  .729086755   Prob > F        =    0.0000
    Residual |  314.895721     1,704  .184797959   R-squared       =    0.0226
-------------+----------------------------------   Adj R-squared   =    0.0169
       Total |  322.186589     1,714  .187973506   Root MSE        =    .42988

-------------------------------------------------------------------------------
------
      dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
    1.placebo_7days |  -.0201024   .0445857    -0.45   0.652    -.1075508    .0
> 673461
  placebo_time_zero |  -.0057942   .0069862    -0.83   0.407    -.0194967    .0
> 079082
                    |
      placebo_7days#|
c.placebo_time_zero |
                 1  |   .0106177   .0118255     0.90   0.369    -.0125762    .0
> 338117
                    |
            country |
          Honduras  |   .0916239   .0322572     2.84   0.005      .028356    .1
> 548918
          Paraguay  |   .0340798   .0346492     0.98   0.325    -.0338797    .1
> 020394
Dominican Republic  |   .1618026   .0303808     5.33   0.000      .102215    .2
> 213902
                    |
           citysize |
        Large City  |   .0172469   .0372087     0.46   0.643    -.0557325    .0
> 902264
       Medium City  |   .0357683   .0370282     0.97   0.334    -.0368572    .1
> 083937
        Small City  |   .0670717   .0399545     1.68   0.093    -.0112933    .1
> 454367
        Rural Area  |   .0326279   .0348348     0.94   0.349    -.0356956    .1
> 009513
                    |
              _cons |   .6444984   .0359742    17.92   0.000     .5739401    .7
> 150566
-------------------------------------------------------------------------------
------

. 
. //Fictional Election Placebo Test, Fixed Effects Plus Covariate Adjustment
. 
. eststo m_5: reg dummytrustusg i.placebo_7days i.country i.citysize male age e
> ducation working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,424
-------------+----------------------------------   F(16, 1407)     =      3.66
       Model |  10.7783093        16  .673644328   Prob > F        =    0.0000
    Residual |   259.19641     1,407  .184219197   R-squared       =    0.0399
-------------+----------------------------------   Adj R-squared   =    0.0290
       Total |  269.974719     1,423   .18972222   Root MSE        =    .42921

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       1.placebo_7days |   -.015148   .0235895    -0.64   0.521    -.0614224   
>  .0311264
                       |
               country |
             Honduras  |   .1120361   .0349068     3.21   0.001     .0435612   
>   .180511
             Paraguay  |   .0331687   .0372381     0.89   0.373    -.0398795   
>  .1062169
   Dominican Republic  |   .1559134   .0328703     4.74   0.000     .0914334   
>  .2203935
                       |
              citysize |
           Large City  |   .0111645   .0393784     0.28   0.777    -.0660822   
>  .0884112
          Medium City  |   .0633356   .0405939     1.56   0.119    -.0162955   
>  .1429666
           Small City  |   .0895626   .0440268     2.03   0.042     .0031974   
>  .1759278
           Rural Area  |   .0880945   .0404289     2.18   0.029     .0087871   
>  .1674019
                       |
                  male |   .0208559   .0240057     0.87   0.385     -.026235   
>  .0679468
                   age |  -.0012889   .0008169    -1.58   0.115    -.0028915   
>  .0003137
             education |   .0097222   .0032614     2.98   0.003     .0033245   
>    .01612
               working |  -.0350697   .0251495    -1.39   0.163    -.0844042   
>  .0142648
voted_lastpresidential |   .0257625   .0288814     0.89   0.373    -.0308928   
>  .0824177
       householdincome |    .001099   .0026614     0.41   0.680    -.0041217   
>  .0063197
        voteregistered |  -.0223106   .0488437    -0.46   0.648     -.118125   
>  .0735037
               remesas |   .0119895   .0280782     0.43   0.669      -.04309   
>  .0670691
                 _cons |   .5872623   .0730699     8.04   0.000     .4439246   
>     .7306
-------------------------------------------------------------------------------
---------

. eststo m_6: reg dummytrustusg i.placebo_7days##c.placebo_time_zero i.country 
> i.citysize male age education working voted_last household voteregistered rem
> esas

      Source |       SS           df       MS      Number of obs   =     1,424
-------------+----------------------------------   F(18, 1405)     =      3.38
       Model |  11.1904776        18  .621693199   Prob > F        =    0.0000
    Residual |  258.784242     1,405  .184188072   R-squared       =    0.0415
-------------+----------------------------------   Adj R-squared   =    0.0292
       Total |  269.974719     1,423   .18972222   Root MSE        =    .42917

-------------------------------------------------------------------------------
---------
         dummytrustusg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
       1.placebo_7days |  -.0044672   .0492784    -0.09   0.928    -.1011344   
>     .0922
     placebo_time_zero |  -.0092154   .0076515    -1.20   0.229    -.0242249   
>  .0057942
                       |
         placebo_7days#|
   c.placebo_time_zero |
                    1  |   .0188554   .0130869     1.44   0.150    -.0068165   
>  .0445274
                       |
               country |
             Honduras  |   .1148611   .0349972     3.28   0.001     .0462087   
>  .1835134
             Paraguay  |   .0449734   .0388225     1.16   0.247    -.0311829   
>  .1211296
   Dominican Republic  |   .1505962   .0332107     4.53   0.000     .0854484   
>  .2157441
                       |
              citysize |
           Large City  |  -.0058766   .0409986    -0.14   0.886    -.0863016   
>  .0745485
          Medium City  |   .0536151   .0411969     1.30   0.193     -.027199   
>  .1344292
           Small City  |   .0837211   .0442429     1.89   0.059    -.0030681   
>  .1705103
           Rural Area  |   .0811633    .040692     1.99   0.046     .0013398   
>  .1609868
                       |
                  male |   .0212195   .0240063     0.88   0.377    -.0258725   
>  .0683116
                   age |  -.0012892   .0008169    -1.58   0.115    -.0028917   
>  .0003134
             education |   .0098345   .0032633     3.01   0.003     .0034329   
>   .016236
               working |  -.0350943   .0251538    -1.40   0.163    -.0844373   
>  .0142487
voted_lastpresidential |   .0253771   .0289061     0.88   0.380    -.0313267   
>  .0820809
       householdincome |   .0009209   .0026644     0.35   0.730    -.0043058   
>  .0061476
        voteregistered |  -.0211352   .0488459    -0.43   0.665     -.116954   
>  .0746837
               remesas |   .0128691   .0280853     0.46   0.647    -.0422246   
>  .0679629
                 _cons |   .5527651   .0790087     7.00   0.000     .3977773   
>  .7077529
-------------------------------------------------------------------------------
---------

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure B.7
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) ,  ///
>                         drop(*.country *.citysize male age householdincome ci
> tysize education working  voted_lastpresidential voteregistered remesas _cons
> ) xline(0, lpattern(solid)) byopts(row(1)) levels(95 90)      ///
>                         bylabels("± 7 days, Naive" "± 7 days, FE" "± 7 days, 
> FE + Cov.") subtitle(, size(small)) nokey       ///
>                         coeflabel(1.placebo_7days = "Placebo Election"  ///
>                         1.placebo_7days#c.placebo_time_zero = "Placebo Electi
> on*Days" _cons = "Constant") ///
>                         aspect(.4) mlabgap(*2) 

. 
. addplot 1: , b1title("Effect on Trust in US Gov't", size(small)) norescaling

. addplot 2: , b1title("Effect on Trust in US Gov't") norescaling

. addplot 3: , b1title("Effect on Trust in US Gov't") norescaling

. 
. //Save Figure B.7
. graph save Figure_placebo2BINARY.gph, replace 
(file Figure_placebo2BINARY.gph saved)

. graph export Figure_placeboBINARY2.png, replace 
(file Figure_placeboBINARY2.png written in PNG format)

. 
. drop sample_reg

. 
. 
. *Figure B.8 Placebo Test (Foreign Govs & Orgs) with Binary DV 
. 
. eststo clear

. 
. //Trust in China (Binary DV)
. eststo m_1: reg dummytrustchina i.posttrump_7days i.country i.citysize male a
> ge education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =       766
-------------+----------------------------------   F(16, 749)      =      3.40
       Model |  12.2902885        16   .76814303   Prob > F        =    0.0000
    Residual |  169.100051       749  .225767758   R-squared       =    0.0678
-------------+----------------------------------   Adj R-squared   =    0.0478
       Total |  181.390339       765  .237111555   Root MSE        =    .47515

-------------------------------------------------------------------------------
---------
       dummytrustchina |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0077328   .0377299     0.20   0.838    -.0663362   
>  .0818017
                       |
               country |
             Honduras  |   .0087989   .0612108     0.14   0.886    -.1113662   
>  .1289639
             Paraguay  |   .0440812   .0592163     0.74   0.457    -.0721686   
>  .1603309
   Dominican Republic  |   .0615556   .0626538     0.98   0.326    -.0614424   
>  .1845535
                       |
              citysize |
           Large City  |  -.0238269   .0835995    -0.29   0.776     -.187944   
>  .1402903
          Medium City  |  -.0792656    .071775    -1.10   0.270    -.2201698   
>  .0616385
           Small City  |  -.0020401   .0829687    -0.02   0.980     -.164919   
>  .1608388
           Rural Area  |  -.1050192   .0726668    -1.45   0.149    -.2476741   
>  .0376356
                       |
                  male |   .1004832   .0375173     2.68   0.008     .0268317   
>  .1741347
                   age |  -.0005059   .0013067    -0.39   0.699    -.0030712   
>  .0020594
             education |   .0203702   .0050101     4.07   0.000     .0105348   
>  .0302057
               working |    .066529   .0376463     1.77   0.078    -.0073758   
>  .1404337
voted_lastpresidential |  -.0116977   .0438528    -0.27   0.790    -.0977868   
>  .0743913
       householdincome |  -.0015376   .0041332    -0.37   0.710    -.0096517   
>  .0065764
        voteregistered |  -.0454027   .0743331    -0.61   0.542    -.1913287   
>  .1005233
               remesas |  -.0146546   .0427332    -0.34   0.732    -.0985457   
>  .0692365
                 _cons |    .445814   .1103268     4.04   0.000     .2292274   
>  .6624006
-------------------------------------------------------------------------------
---------

. eststo m_2: reg dummytrustchina i.posttrump_7days##c.time_zero i.country i.ci
> tysize

      Source |       SS           df       MS      Number of obs   =       924
-------------+----------------------------------   F(10, 913)      =      2.30
       Model |  5.45270485        10  .545270485   Prob > F        =    0.0114
    Residual |  216.387122       913  .237006705   R-squared       =    0.0246
-------------+----------------------------------   Adj R-squared   =    0.0139
       Total |  221.839827       923  .240346508   Root MSE        =    .48683

-------------------------------------------------------------------------------
------
    dummytrustchina |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Int
> erval]
--------------------+----------------------------------------------------------
------
  1.posttrump_7days |  -.0389954   .0736112    -0.53   0.596    -.1834623    .1
> 054715
          time_zero |   .0179905   .0125524     1.43   0.152    -.0066444    .0
> 426255
                    |
    posttrump_7days#|
        c.time_zero |
                 1  |   -.023736   .0174985    -1.36   0.175     -.058078    .0
> 106061
                    |
            country |
          Honduras  |   .0030983   .0568481     0.05   0.957    -.1084698    .1
> 146664
          Paraguay  |   .0133263   .0536147     0.25   0.804    -.0918961    .1
> 185488
Dominican Republic  |   .0656114   .0596589     1.10   0.272     -.051473    .1
> 826959
                    |
           citysize |
        Large City  |   .0050478   .0792262     0.06   0.949    -.1504387    .1
> 605343
       Medium City  |  -.1032392   .0666967    -1.55   0.122    -.2341359    .0
> 276575
        Small City  |  -.0141559   .0768775    -0.18   0.854    -.1650332    .1
> 367213
        Rural Area  |  -.1627203   .0659516    -2.47   0.014    -.2921548   -.0
> 332859
                    |
              _cons |   .7328159   .0657985    11.14   0.000      .603682    .8
> 619499
-------------------------------------------------------------------------------
------

. 
. //Trust in United Nations (Binary DV)
. eststo m_3: reg dummytrustun i.posttrump_7days i.country i.citysize male age 
> education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,217
-------------+----------------------------------   F(16, 1200)     =      3.52
       Model |  11.3336612        16  .708353822   Prob > F        =    0.0000
    Residual |  241.354917     1,200  .201129098   R-squared       =    0.0449
-------------+----------------------------------   Adj R-squared   =    0.0321
       Total |  252.688578     1,216  .207803107   Root MSE        =    .44847

-------------------------------------------------------------------------------
---------
          dummytrustun |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |  -.0248734    .027562    -0.90   0.367    -.0789484   
>  .0292016
                       |
               country |
             Honduras  |   .0669488   .0426218     1.57   0.117    -.0166728   
>  .1505703
             Paraguay  |    .025777   .0427007     0.60   0.546    -.0579995   
>  .1095534
   Dominican Republic  |   .1190636   .0453476     2.63   0.009     .0300943   
>   .208033
                       |
              citysize |
           Large City  |   .0428141   .0617269     0.69   0.488    -.0782905   
>  .1639187
          Medium City  |   .0444122   .0515345     0.86   0.389    -.0566956   
>    .14552
           Small City  |   .0626473   .0569281     1.10   0.271    -.0490424   
>  .1743369
           Rural Area  |   .0447349   .0516093     0.87   0.386    -.0565196   
>  .1459893
                       |
                  male |  -.0035738   .0275449    -0.13   0.897    -.0576153   
>  .0504677
                   age |  -.0006024   .0009661    -0.62   0.533    -.0024978   
>   .001293
             education |   .0165757   .0036429     4.55   0.000     .0094285   
>  .0237228
               working |   .0110443   .0285495     0.39   0.699    -.0449682   
>  .0670568
voted_lastpresidential |    -.04906    .032399    -1.51   0.130     -.112625   
>   .014505
       householdincome |  -.0003341   .0030841    -0.11   0.914     -.006385   
>  .0057168
        voteregistered |   .0752097   .0565907     1.33   0.184     -.035818   
>  .1862374
               remesas |    .036992   .0323098     1.14   0.252    -.0263981   
>   .100382
                 _cons |   .4501018   .0815784     5.52   0.000     .2900497   
>  .6101539
-------------------------------------------------------------------------------
---------

. eststo m_4: reg dummytrustun i.posttrump_7days##c.time_zero i.country i.citys
> ize male age education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,217
-------------+----------------------------------   F(18, 1198)     =      3.13
       Model |  11.3381053        18  .629894739   Prob > F        =    0.0000
    Residual |  241.350473     1,198  .201461163   R-squared       =    0.0449
-------------+----------------------------------   Adj R-squared   =    0.0305
       Total |  252.688578     1,216  .207803107   Root MSE        =    .44884

-------------------------------------------------------------------------------
---------
          dummytrustun |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   -.027992   .0586784    -0.48   0.633    -.1431159   
>   .087132
             time_zero |  -.0003054   .0103197    -0.03   0.976    -.0205521   
>  .0199413
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |   .0017055   .0140433     0.12   0.903    -.0258466   
>  .0292577
                       |
               country |
             Honduras  |   .0664666   .0427813     1.55   0.121     -.017468   
>  .1504011
             Paraguay  |   .0255923   .0428927     0.60   0.551    -.0585609   
>  .1097455
   Dominican Republic  |   .1192125   .0455698     2.62   0.009      .029807   
>  .2086181
                       |
              citysize |
           Large City  |   .0428019   .0617955     0.69   0.489    -.0784376   
>  .1640413
          Medium City  |   .0448549   .0517029     0.87   0.386    -.0565834   
>  .1462932
           Small City  |   .0629511   .0572078     1.10   0.271    -.0492876   
>  .1751897
           Rural Area  |   .0452541   .0517881     0.87   0.382    -.0563513   
>  .1468595
                       |
                  male |  -.0034128   .0275889    -0.12   0.902    -.0575408   
>  .0507152
                   age |  -.0006046   .0009674    -0.62   0.532    -.0025025   
>  .0012933
             education |    .016579    .003647     4.55   0.000     .0094239   
>  .0237342
               working |   .0107504   .0286477     0.38   0.708    -.0454549   
>  .0669558
voted_lastpresidential |  -.0491889   .0324402    -1.52   0.130    -.1128348   
>  .0144569
       householdincome |    -.00032   .0030882    -0.10   0.917    -.0063788   
>  .0057389
        voteregistered |   .0755598   .0566908     1.33   0.183    -.0356644   
>   .186784
               remesas |   .0366163   .0324905     1.13   0.260    -.0271283   
>  .1003608
                 _cons |   .4485959   .0915147     4.90   0.000     .2690489   
>  .6281428
-------------------------------------------------------------------------------
---------

. 
. //Trust in Organization of American States (Binary DV)
. eststo m_5: reg dummytrustoas i.posttrump_7days i.country i.citysize male age
>  education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,232
-------------+----------------------------------   F(16, 1215)     =      4.35
       Model |  14.5062179        16  .906638617   Prob > F        =    0.0000
    Residual |  253.490535     1,215  .208634185   R-squared       =    0.0541
-------------+----------------------------------   Adj R-squared   =    0.0417
       Total |  267.996753     1,231  .217706542   Root MSE        =    .45676

-------------------------------------------------------------------------------
---------
         dummytrustoas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   .0162669   .0280131     0.58   0.562    -.0386926   
>  .0712264
                       |
               country |
             Honduras  |   .0488153   .0452743     1.08   0.281    -.0400091   
>  .1376397
             Paraguay  |   .1237173   .0450158     2.75   0.006     .0353999   
>  .2120346
   Dominican Republic  |   .1692139   .0479651     3.53   0.000     .0751103   
>  .2633176
                       |
              citysize |
           Large City  |   .0294447   .0645644     0.46   0.648    -.0972254   
>  .1561149
          Medium City  |   .0781925   .0544995     1.43   0.152     -.028731   
>   .185116
           Small City  |   .0837183   .0593996     1.41   0.159    -.0328189   
>  .2002555
           Rural Area  |  -.0119917   .0539905    -0.22   0.824    -.1179166   
>  .0939332
                       |
                  male |   .0244919   .0280561     0.87   0.383    -.0305519   
>  .0795357
                   age |  -.0013838   .0009603    -1.44   0.150    -.0032679   
>  .0005002
             education |    .010416   .0036814     2.83   0.005     .0031934   
>  .0176386
               working |  -.0316747    .028886    -1.10   0.273    -.0883466   
>  .0249971
voted_lastpresidential |   -.019564   .0333437    -0.59   0.557    -.0849815   
>  .0458536
       householdincome |  -.0014704   .0031098    -0.47   0.636    -.0075716   
>  .0046308
        voteregistered |   .0641668   .0565637     1.13   0.257    -.0468066   
>  .1751402
               remesas |   .0717125   .0334986     2.14   0.032     .0059911   
>   .137434
                 _cons |   .4653091   .0829792     5.61   0.000     .3025107   
>  .6281075
-------------------------------------------------------------------------------
---------

. eststo m_6: reg dummytrustoas i.posttrump_7days##c.time_zero i.country i.city
> size male age education working voted_last household voteregistered remesas

      Source |       SS           df       MS      Number of obs   =     1,232
-------------+----------------------------------   F(18, 1213)     =      3.91
       Model |  14.6965143        18  .816473015   Prob > F        =    0.0000
    Residual |  253.300239     1,213  .208821302   R-squared       =    0.0548
-------------+----------------------------------   Adj R-squared   =    0.0408
       Total |  267.996753     1,231  .217706542   Root MSE        =    .45697

-------------------------------------------------------------------------------
---------
         dummytrustoas |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
> Interval]
-----------------------+-------------------------------------------------------
---------
     1.posttrump_7days |   -.027728   .0589188    -0.47   0.638     -.143322   
>  .0878661
             time_zero |   .0040642   .0100735     0.40   0.687    -.0156993   
>  .0238276
                       |
       posttrump_7days#|
           c.time_zero |
                    1  |   .0046288   .0140419     0.33   0.742    -.0229203   
>  .0321779
                       |
               country |
             Honduras  |   .0464418   .0454052     1.02   0.307    -.0426397   
>  .1355232
             Paraguay  |   .1206787    .045188     2.67   0.008     .0320234   
>  .2093341
   Dominican Republic  |   .1721962    .048164     3.58   0.000     .0777022   
>  .2666901
                       |
              citysize |
           Large City  |   .0279921    .064647     0.43   0.665    -.0988401   
>  .1548244
          Medium City  |   .0814028   .0546277     1.49   0.136    -.0257724   
>  .1885781
           Small City  |   .0881734   .0596218     1.48   0.139    -.0287998   
>  .2051466
           Rural Area  |  -.0078364   .0542088    -0.14   0.885    -.1141898   
>   .098517
                       |
                  male |   .0249183   .0280748     0.89   0.375    -.0301623   
>  .0799989
                   age |  -.0014143   .0009613    -1.47   0.141    -.0033002   
>  .0004717
             education |   .0104388   .0036843     2.83   0.005     .0032105   
>  .0176672
               working |  -.0338841     .02901    -1.17   0.243    -.0907996   
>  .0230313
voted_lastpresidential |    -.01925   .0333604    -0.58   0.564    -.0847005   
>  .0462004
       householdincome |   -.001424   .0031117    -0.46   0.647     -.007529   
>   .004681
        voteregistered |   .0673551    .056693     1.19   0.235    -.0438722   
>  .1785824
               remesas |    .071578   .0336101     2.13   0.033     .0056376   
>  .1375184
                 _cons |   .4782764   .0913819     5.23   0.000     .2989923   
>  .6575605
-------------------------------------------------------------------------------
---------

. 
. //Generate variable capturing the sample included in the main analyses
. gen sample_reg = e(sample)

. 
. //Generate Figure B.8
. 
. coefplot        (m_1, msize(medsmall)) (m_2, msize(medsmall) mcolor(gs9) ciop
> ts(lcolor(gs9 gs9))) || ///
>                         (m_3, msize(medsmall)) (m_4, msize(medsmall)) || ///
>                         (m_5, msize(medsmall))  (m_6, msize(medsmall)) ,  ///
>                         drop(*.country *.citysize male age householdincome ci
> tysize education working  voted_lastpresidential voteregistered remesas _cons
> ) xline(0, lpattern(solid)) byopts(row(1)) levels(95 90)      ///
>                         bylabels("A. Trust China, ± 7 days" "B. Trust UN, ± 7
>  days" "C. Trust OAS, ± 7 days") subtitle(, size(small)) nokey  ///
>                         coeflabel(1.posttrump_7days = "Treatment group" ///
>                         1.posttrump_7days#c.time_zero = "Treatment*Days" _con
> s = "Constant") ///
>                         aspect(.6) mlabgap(*2)

.                         
. addplot 1: , b1title("Effect on Trust", size(small)) norescaling

. addplot 2: , b1title("Effect on Trust") norescaling

. addplot 3: , b1title("Effect on Trust") norescaling

. 
. graph save Figure_placebogovsBINARY.gph, replace 
(file Figure_placebogovsBINARY.gph saved)

. graph export Figure_placebogovsBINARY.png, replace
(file Figure_placebogovsBINARY.png written in PNG format)

. 
. drop sample_reg

. 
. **The End**
. 
. **Questions? Feel free to contact the authors at rbateson@uottawa.ca and ml.w
> eintraub@uniandes.edu.co
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/gina/Dropbox (Personal)/Article Outlines/Trump Effect Abroa
> d/LAPOP 2016 original datasets/bateson_weintraub_trumpeffect.smcl
  log type:  smcl
 closed on:   8 Aug 2021, 19:09:34
-------------------------------------------------------------------------------
